{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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XvesnAJjctpNaV9vfjQ1Jou+PowgC7dt8dQtBaCeqAUTbljguJumt9RjXVZ4jiRBZGrO5\nMQ19tmvo8wBjwIj9jGDsM3/8nm+5KI1HnvmC6Y2d4zh9fmAy2wCCSI+vmPQeRyRt3fn4gSPSf5Ex\nSI9Pb6Ls3DibN52LV83ueNlFaRxqpoYYYoghhhhiiCEuA1dUMxUGnVTqM0ZI7GdBNmkjeiKg9BUB\nOE5f8HRxUo2V47rpO0Uko5wQFaUBlxinJ58aQ9KTxsXBtfKkcQQnlcBN+n6TmPNKw+eCMcK+m/YB\ncOCppyiV9PSU84RiUU+rb/621/At92hS5drGBgeefgaARw4cpNnSU2Gn0yUKE9tvEQVX27O2fIZu\nR/Oq+Ri2TezRe2prePY07AJBrCeRbhAS2hNr3Epw7HuqlSpde1qpNWs4SW4g+srlMjftuwEARwym\np8ARQ2K1KomJ6JWCMsT0y0LFqm2i90xmcFNNlsH0tIWA2BJYJnGIbXvjuJveLyI4VkOUvg8d+sSe\nFAVhfb0+EH3ngpzjE2kL7S+Y3sm40Jt2OHRTbRo4mN5JUfqau3SCA0Yc1rr68Ce+dIStk/rOkyeX\nabRUM1n1JO1CMYbEnrDjMMSVwedp0A2I43OV68pQ9zyCU/rr+/zr5lwarkF+73zarfPBSUKCoKdd\nSGjWVgBYXDzDlw8eBeDoydN4OZ0/pptgkp72NCYOtX8KpQLX7dTKG6uHj7BeUy1nvlRi3/W6Fo4d\nOkyn2bZPOhirsYrxiey87QQRxfIIAFP7bqS1oZU7njw2z3JHtdCjox6l3GBrsZArpjSNVMp4hTKg\n+tht221715bZWNa8hq7vU6s3AdjpFSiUVPM2OT5OYvngaKVEEKi2q1TI09Punjh5nB22D3L5PMWC\n0lcu5elYtfaDjz/BeEWrfSycPsXhZw4CEMUxOV+vuzmPkZHqQPQBeJ6XjqFIf59IkoQ46lk5ILF9\nLAImVP7ouD6eVbHocFj+lARElsdEUUinrfT645NUrMatHXZYWrHX837KxzGCYw0U7Y7L+rqWnZyY\nmMCzGrG1jTr58uDVZtrNNVqn1CIRNGu0GzoXquNzrJzUObJtx07yZf3dehe6kf7W0snP8Nz9/x2A\nqV2vZO4OzXUppTGw45gEKziFGdsnRU4fekA/x11m9qiFJBYPcfr7X0/rZIxsWpdR0rPYSNZocHE4\nGQuFJJv37Awf7SGr8b4QV5AM/0y3+OybRL6Oa/d/9+s/Xw6uqDCV89w+lYZNQlN/c3RwevuMOKlJ\nznUzAhQG1woFmxizk932JN1MozDGTX8MIruJiOPiZL7omY8cIWWqxphUrauJ0y+MZqvOnuu1fNXk\n5BQHnz4EwMz0HJPTOqHntm3nqQNPA3DwmYOcOqVqWvFKdK05r1AoUqrqgkwSQ97XNq+tLPHMaWWO\nd+y/lZVlfXZtYYFtO7Qs0UazRdcyi8JIlcSqqINWkzixCW49l0ZXF1tkEvwBdZQmSYh7NkFJsNpm\nxElIbPLcJIkz5psIg143RPRmvIio2QxljCazZFIhSDzETlGTeCS27VHcoTdfHMdJF7WIm5p/jTEp\nrcYknLvO54A099qMgaxJUfqMRezcSEwREf2tdrtFIWXCMUmPsTt90yE4qfDl5jyePKDM89ipDaZK\nSnsQdGl3dD6OlZ2ekQkjfQEyCWNcGVzRvFl9f2FT3rlwqQwoK2T15sYg6vXLgQkC4sj+VmwImypI\nPPzggzz0nJq7IiSdMyJJ31KQeY/j+ezZqwmvo/UWh09oebd9u3az/Xq9/tQzR+jYjSb0PKwli1on\nZGtFr0dhBxPrmEaVcZquVrpYbjWJNrQNza7H9FhafuyCKOYLbN26y352qdfUtLd1divdUPlIN4jo\nWl62vrDE5LTyJs+NkVCFqbWNDXxf56+f98jn9VAWxyEn5pXXTE1PMTo2pn1jPBy7dsfGq+Rd7WM/\nV6TkKR3lPVt55+ibAXj86SMsretvBVFMoZAxmV0EuVwunYfdbjedO66bpPuB57uIKI2JiRDLx5M4\nJO6xocTH8/SeatlndUPngokj8JT2QrFA2QqDSSNOD9d536NsD8Ku65HLq5lsZR3CQAXrkeoYOSsE\nr200KBU3Jd6+IA4dfBgTK08Nuq10/8uNVMnZqVDO58k5ys8KOZ/Ti8cBOP74H1Jb0NrG9WN/SL6k\n7dz+8nezfuRBAI498jF2vuzvARAlHY498KcALJ88xC2v/F4A9r78HTiWJ2UPQgaTKiUSMXh2jsdx\nl7g7eHJ/JzFIzzUgc12cC5n3v36Pz5r8kiSBHi9xHFxr6sVxiNNDFJuFvlSn0jcjqgRu0q+TTQ0c\nmMShmW+IIYYYYoghhhjicnBFNVPlUvG8DqdZ9W2qAoT0BKQaJHt/EmeOjk7qBAiSqv1MxiHaOELi\n9DUWxvPT90tGU5K+3hgS6yjvug5RPHjpqXKpQGAl9lazw513vRyAV736dXz5AS0h+B9/6z+x/7Zb\nACiVSkzNqEN3s96lIaot8pwcrYaaDZrNFt22qn5nZqaY3WIdwF2fU4t6wjYJHLFO1vlyifV6A4Bo\no051XB1QTRiwtKinmNWVFdycnkRK1RI7du4emMZUBUzG5JQIYm1+kph+2U1xEOlpYbIaKEmPDMYk\nGHuylIySNo6j9MSQOSChZ4Be4IBg3PSN6hgOuI6Tah2TOMl6OQ5AoDmX5vki6Dk8uqyvrgLwMz/9\ni9xpAw3+wY/8ffK+bY+bA3uKzRW99PQcmYiP/e9HAQg60PGts61RbR9AFISp+SmJIrBO5E4YQn7w\nE7+S2ddMbVa7f/0953v2+Vx3sqbNb2Seu1joWfQ9PLrWsfrA40+yEql2wSvkUp7kieBZTbhxIbZB\nBZ0k4dAJ1RiudQJmd2gt1lJ1mkPHFwGY32jTCXo/5tJTEo5EIYnlYXHQIG/JXVxtULd8wi1UWEv0\nt1aDAiu1wcaxWCwinpoNJQmpjOhz5fIY9SXVXORyORyredm6dTfXX3eT3m+6rFvTcWwCjF2L4+NV\nigW9f73eSpdNEBgqpZLtp74GuNEMOVNbAqDVaFJvKN8JojZuUTVZzUaHtWXlU5WCB9XB6z9nzXxR\nFKXzs1AsUrAO6KVyHnF0fbRadXxfOznnuxRyalL0/SKOq/Q6LiyeUV65sbxCftZqv8XQ6qoGrdtp\nY+zaSsKIxJoOJTFElscILqVS1bYzR48HJIn5umCMC+HA5++lvb5q3x+lY9GtneT4wx8HYO/1N1Ga\n0GCAk0e/yuoJDU5pzp+m09T7O/UaRx7Qkp+d1ioLz+o9tYWTjJTVBBy2lqgftXP52HGOFT8JwL67\n30gub0tmxlGfN6jqSNvmepx46gsABJ06rY2e68QbLkqjg0PS22sNKT82mWAl/bv/TNYlKA0+y3jy\neJ6fPhC2uywcek77bW2dHS/W/TWuVnBC6+6TdWTP6p6zmnnOYvmXwJ6uqDDlOE7qD0PWJppkGa3b\nJ8aY9LvIxOn1OA7TznXEZFTzkm46SZKkEzoxQtcujCiKUtOh7zg4Tk+YIqM+7C/aJEk2j/BFMLd9\nmvlTJy1dwqvueT0A9372syysatTQ/jtux/e1Dd3IpOrkQq5I1zK42mqd6WllOi950e1s3aoRf7fs\nv4XR8SkAqtUK1nUBF0O3rfQ2mk1WV5R5ffFLn+dzX9Comlq9ScsKWWEnwVj/nNNn5gkbA5rBRCA1\npSU4PQEKByfVpwoJygCN/U7h9QXcJBPBiQGnJzQ59KZlPl+gUFBTZxgkBIEytHanX1vVEZOaF8Ux\n9KLqEMF1rWAlbqYNA8BwSWOuRPYcpQytjvbDyonTfOhrvwvAmVPr/NTP/CAAE5UpyKmp6OEHnuSZ\np1XAffTJ43zk3mPaZmNYqGubY69O4+CTAMyO+RgbwUcUE9mNxvg53OLEwE32/T4jOlugyUbqZa+d\nK4Lv+QhDFzMvXuy7QZGECVGg74kdaNvDSUe60PMvMpnZ6brkrZkqdmMiR+dnNwp54JFHAHCaHW67\nSRl1p5Nw4LCa61vGg4qaWCLH6QVSkeSEXLHHzOuU7Zwcra2T2KjcZn6CemUcgFpxmuaAG3G5UsGz\nJrlOs07HCgLHThwjCJXWSrHE3jk17U3MzrG4pPOu3qjj5fTZgl/Atf6WC2fO0LQ+O3glZqZ2WJrq\nRLH2zdYtU5R9XWcbjSYtG0F2YqnBypoKBWEM7bb6E90wPc7I3CwAzy4t47YaA9Gn7dwgDHv+n91U\ngPI9T010QNgF1x6cfDdH0R4q/LxPq21N/Z0u5ZzeP1YtUbD8ZqSSx7M+DmEckLP8wyQhJL1I7BZh\nx/rV+T44Pf5UYmJS6cLNpQJ0tVql2RicxrCxAtY1wBhIeoeoSsjSST1crddPEBeVxsfv+wPaq+re\n0enEtJZVqOm0ItZPqHBfX/xTpKvt3LFzNzvyKghXZm7FbFjhfqNB0lJzpxMHlAs6B5K4r6yIkpBe\nBH6uWKJxWoX0oNMiuAQlg3HcdC/XPaFvTz83L5FUqBSRlH+rQKS/u3b8JPMPK19cfvwg6y2dt82F\nFV78mlcDcOv3vB2p6FqP40Sj/ugd2vvrLMtj+jH9l4ahmW+IIYYYYoghhhjiMnBFNVPG9DMEiesg\nNhrDhTT6xWTkws35hAxdqxY3SYhjT0ZeJs9UHCebTr1ZQddJ/983Tfmel6r7oiTrdOekUnEcxan5\nahCUSyVutifXJ8ND/K8PfRiA8dlRyiN6OujGAYWyqsA3Vmq4VtIu4jE2otdf+tqX8q53vxeArVvn\nqK0r7YurNWJ7MsoVfDpNdTolDNKemxgt8uhDXwNg/75bGLWRJe/7nfcTWu3O9q3bmBhTFfXq8jJL\nG+sD09jXaCSYpGeGdVKNleCm5jY9gfQj2nqKVnVEttF5JsSEOp6zM7u55WbNB7Rt+3bGxvTE3m6H\n1Graxsce/yrHjmsEZLNdw3MzZqOe5iujyRRHLk27semEdGmaF8EwNan9eudLbuSr9+lJ/YP/7YM8\n/oBqN27dP0fXRlg99cRR5td0/OuB0AmsuVMMUaefo+wTf6kq+6mqR906o7fbMWtt1UbcvncLb3vr\n5MDtrFaraU63C0XU9U6lSXL22uqb5c9n0jiXBirrgN6Lfuohe0LNmh2drIn+EjRhvhiCyDpi4xDa\n6FgvZ/Dt7HCN4FttUd5105Or43hpQEuAwSnYgA6/wJFTRwANJMlbbVQhCRF7sg9xUsf3rtMgtmYz\nvBwFa86eKXqUbP/XnS45G6QRSIlwQHbjOi4l6wztmIj1ddVwbtSXGB1XTTZBEwl0Dj751CLtSOfL\n9FiFfN46nZfHMdLrA0kDGaamtrHaUrcD13fZZgNotkyM0bK563IS4dk1Uh6rMjunc7BdXyc2gf3c\nZmpMTUgtJ6AdDRatCNBobqRaCWPiNFdUHLXwXH1PqTiWRs06Tj7VgDRbIaG93m3V8e2YxE6MZ1WH\no6MVInpRwgFhoFw0DDvEqVN4QmjHttVu4Npo6rGZOUZsjiocIei27bMhXILWxikFJKm/hEt+zM47\nE9Je1mjNE89+memmaq3PHHsMYrtvbYQU7IRxfJ+wbufvukeuoHOjvlrnuaOqkbzuxhsYmdQ9ZmSq\nH1UZLBxnI2mnfVsqKt8tlEZxbP7FTvsMQWfJfm5THZsdmEYyGZ/Ox4s3R9j1c1HFUcizx5T2uBOw\n8aWHtR8+9UWCeTVZBhLRffFubXOhyIE/+YTSfnKRm/+uBkKM33odof0JP+61SXEubfyl4ooKU51u\nJ92acq5Dr3PjOKNucxwiO1HCKKBtE9h5npea8DyPdOPOhparU34/Uij1t8rc43tx1g2HKOpvFj3z\nXxTHdHrpBzwf7xKipCqlab76oG6aC6sr7LpxNwClksvEuPo6La2ucfTIUQCKuTKVqgpZG6tr3HLL\niwD46X/xs/hWNbteaxJYx6DK6DidQDeIKIxBVO1dawfMn9B3djdO8Uv/5ucB6HTa/OAP/igA3//d\n76TR0MV54OCTLK7oRKyUJykPauWjP+Ec6QudxvTNf5D0zWripNGQxvQTV6oTlN6T9/PcfPPtALzi\nZa9jelo3gjCKUmEqny+wuqp+DuVyleuv3wvAVx74HKvrusDV385GK0qCsW1wLmH8AMQk/QBOMu5T\nIv2IlCQLfYQcAAAgAElEQVShZ8sxkqQRi5gO1nrCHXe/iCcf/SoAu2erHHhaTUL33v8Yk2VldD/2\n1uv55BOqsnfWIzqJ9UVp9E2Z3djlv/6VbuBxHBMmvXUjdGzai++swVvfPHjEYqNeP6cQlDXhZQWZ\nbKK9s81857ruOM45TXVnR/OdK5VCFEX9SC3P28TgQuu7MggcNyGxG2Uz8mkaZXedMMLNWVOK+BSs\nD6XjuqkbgusIBbuJuJKwY5eayqTd5aEvfxmA2+56MXN7NJru8/d/hcgy5yiRdKMPgfk1FTxmdozh\n26gwJ+/gWjPVRM6hG9iDVr1Du9EZiL5GEJDUrK+NE1OtqsDi+wX8qj2EbHis2d85s7ZEuaD92moJ\n40VrKo+K1K3gPjk2ioiai2MMG6sqoHWjiMNHlI5Tpx1q1mQ6O1NhRHN2MrqlynhV/xifvJuxGe2z\nUnmUIwe0z6af+DQPHBy89vPkxBgry0ojJiJnTXITU8We2yGFQkTTRmp2gjCN9PY8h+qI+pQ5U2MU\nEhWOlk4v0uzoOBdHJ9LIr6XFxdTbIAm66UGi3W6nfM4Rh9aqHmDz5RGKIzY6M4rZsH5P3XaH6amZ\ngWkMAKyQmEiCleEIVuOUDz3yyY9TnNB5ur60Bl3bnjVSf+CkBGHbCnHdLl3rarFwZoUjZ5TXH3zy\nKexUo91tUzW6fzz6sf9BXFDi12pn8Es6f17z1nez3QrRn/j4f+PYE3pIj4KEHTZ6fDBs5hMpZ8ju\n2bh4NsmqIUSkd/jxaaxoQuLPvv9DjNz/BADVoENs54PkCwSH1MTpbpukkOhcPfKFz1Gz/nRv3PkP\n8cdU+DVxSDZpwiaXhvTapbkxDM18QwwxxBBDDDHEEJeBK+uALk56sndjg2NPhEESaMQVGs3Q0xYF\nYUDbRuD4vs+4VRXnci6ePTWefQLunWg35abIaKYcx+k7pmdO5q7r9k/GcZwaphJjiDm3GeNc+PL9\nD7DeUNX46FiFWks1QfVamy1TqnUKg4BSXk8EMxOzGGt+2Lpljne+83u0PV6ORktPFnHiEMXW+brd\nomNNO+12l1ZT76nVm6mWotlqUbT5RjzX59P3qgP6j/+jH+XzXzhsOyJmxZ6kTsyvMDU5+EmqH23p\npNolY0xGznfoufE5ZPJAkXXmN5SKekrYd9PNvOyl9+j9js/RoxqVkcsVUlNUdaSajuOO7TcwY53z\nK5Uyn/j0nwGwsbFKnPSiAsGxuZ8817mkE0aSRJiklxTW0DurJPT9zMUFSfraUSO9sgxxmmj0znvu\n5v6/1Gic08unWNVpwR3XjXPXPj35vf2Vu5jdqmr33/vLJ4ljm8/GxNSsVjaOQ3yr6XDFpE7VSdI/\n7a3UOwQ9x/QBkM/nUy1PHPfzgsVxfE7zHLDpZNnT4m5y3MysIcdxNt3j+/1yH1mzXe9zFEVky030\nridJP5+Q67pfZxq8IFyPWqL3L3Y9jm5YTd+Ij2Pf4/oe4vVcDEhNGoJJj5q+n2NmRtfHdLnC0489\nBsDo6CgzczsBeOixJ2inuWmFxPZDMV+mY/lHNz/OwSXVajx1aoW29JJmFqhaLU6rdZKkMVj+njDo\nkitZzYuTpHO20w44eULNyOWRKtUZbeP28iR1q2kit0aIms0LzgR5S3e73WFh4bR9dpzY5meLmw0W\nQp3AE6N5du9RE8/OXbOEHdUaPPzoQzxttXCeO5EGyuRLLiWrfT9Tg1OnzgxEH8Dk5CixDclcWVlP\ntRXFYg7P71kwupkglIRSUbW+JonpqXlcr0Bok+I1utC09p6kG+HZHFsYk0bKRmHI5KSaLMvlahp5\nnCQGYxMAdzstPKtpXFxeor6h/LRSLFOwgQGDQLoGYwOAEhxcu9aTKEq1J+1ajWJB97+45lC0Foli\nJc9aR53d260AWjbnl+elCZO6tSg1ZUdxhGPXZbga0bTpsFzPob6qGv4zx48RdLR0UNGrsrJHLSqH\nH/w8xDbIJTLUTp8YmEZ1KM/uo72cVhE4vQTPhqUNXR+5nJeaj9vNgHpTx/eZ42fYYy1OuZyLb7WN\nXhAztqzvWQlDutu1na//4XeyUdU1slLv4lsN7PhoGcmwkk2uCClLk0uKAr+iwlSxWCSyPjvZOnpe\nPk+SmcS9SDfHL6RRXq7r4VvfA88h9W2AjFBkSBNBQt/f42ykTFscjH1/EAREduMOowgvZ+vWeV6q\n+h0ECwvzlGyGX8eNmLCJ7qrFMerWL8l3XCas6rSUG6HZVRX1i2+9jZv2auhyGMQkNqSz0+qmwmZ9\nvUa3q33Y7YRp8kcHIWhp+3N+ietvuBmAm27cx/ETyhwfefQAvg1/XViuMz6lDHHrjirlS0gy10sU\naeK4H4np9MNXBaef2nJTBtq+erdQLrFz524ARqtTHD+mbfRch3xRp+X0zDSxXSztdoNqRdteKY3Q\nDbW9N5fu4Myi+gN88b7PEFkfmTiT0M14lxicF0X9FBtJjNgNGaPv0o8GJ5M4lJ6ZT/ppFSa2jLN1\n1zYAFj57OE06umfrCHNT+qLf++sT/L3vfDEA9Q89ns7rbqZagJNRhXuuENq50A2jNIHn6dUOQXdw\nE5ibMZ+dL1IvG4qeNc9lTXtn+09l15zJCM69OXO+NXkhZA9IlyIUd6OEmhWo50OXtbyuxVxlnLyN\n+MoV8mktNxEh1xOyMKkg4TguS8tqYr7txn380A//EACT09McPa7mE8/zKNhnPeOk2blzeSjbJJz+\nxDYWTquA7JoxSi3dlE27xWROx25ucZmoNZgZLOq02Fjv+f5U6Vrz/0ptldUlNXmUna3M24ztJ0+f\nZN/e3QB827e9gS9+9o+1LeVFxit6OOkkpzArKhQUi+MUyyrk1XOnuG679t/0qMPINr1ujAs2+mz7\nzr0Y0U14+cxJVlfVrO1IkbntKtCtrK2xdWrwqFMMzMwonyoXq/i2lqXnFvsJG02MZ/lglEREgY6D\n7xXxjD2EmDxNezjpRgWwWcwb7Yhy6iM2SdhUwaTrCrt2aV8VCgXaNsp6aWmZWVuL0Lgeke3zjdUV\n1tdUGBnZvgvfHTwmzMuXmdih/bl0ZiEVoIwbkFg/r+nJrdx1550AfKV+HxWbkmHHtlmetSkBchHM\n7de+Wgq6lKxAt1HbSIX7W2/dT76ifXj/l74GRX2PlCepWOXG1qBLYOfmnq0z7N2jEZ2Pjo4TRyog\nF4slxicGX4tnRwY7pscHDOt1HZf7vvI1vmj9oUp+kT0zKgStbizh9HzWSj6PWfcH3y0yZg8BAXDS\nSkFn1kLaBV27W6TEtqr2yeJCk/njmkT7ta99BaWqjV7MCnnP32VqaOYbYoghhhhiiCGGuBxcUc0U\nxqQq9TCJ05ItrXabJ2wel2cPPMUt+/cDcOvtL0Fs/hDXd8j6Eadn3iTZFLUVZvJJ9U60vuenEmfW\ntJBN0OVmavwdP3GSJ2y5l5fcdhvX7bluYBI932FyQqX90miBasWWKnBcDjyhEWj58hgjZZXw/87d\nr+DpJ7SG1Xe+/R2MVlQab3VixLNqbDfBs1E7YaVMU/Q05EmOdataJo7AarJKpRHKVotz72c+g2uT\n9m3bvpf9t2tNwI7pcuiI0lhv1li3jqwXgyCpY7chSc22GC9NyobEGQf0fvJUjKFY1Jwfc9t24tvc\nNkeeO0SxoH02MTHJqI0ybDZaqaYm6IR0bKkd13GYnLan1dIEd97+KgCWl1Z4+qDOozAOU+fjnnZr\nUJgg0GR1gLhu30HSGHU8B4zn0TNsGsehpzMWk0dsrppcPsGd0XIj7eizzI6Ubfsj/vtfq+ZgcmqM\nd02olrIWRCytqenHy3ukUZDi0LSm4JGCR2JPdVpPUq+v1QK67cHpDIOgX7fOcVLzWVYDFcdxap7T\nAJC+ZqqHJEky5sIk7Ss1F9rmy+VFyWTN8peimWrHCQ103awmDtGoahT8UhXfJmjz8rlMySo3NYe4\nJsGzY+q6CeWyNcvPzPB33vbtADzzzDMsLOq6GR0bo2FzGgWJEDs9s01As6vjNb/a4PiSTXTYAN9G\nZRYKDgVroqtGAZjBNIyTY6OUK9ah1gScsvntEnGolnSuLa8sMjqmPGWymufuu9Vk43hfYXLO5qWL\nDxM21R2hPNZhyx5te945Qruua7RaqgE6N03lRQSRXl9bqTO7XTVv1fYEBV/bE4VnyPv6u0kSccbm\nJxLjEIWDlyEJOlHqzF8uVdNgpbWVOp7fNyP3clElJkHozdkScdjnPa26zclm8oS9EjVhh3JZx7la\nrqSldybGxynYaLhms4nv5S1dEbGNesuVXVrNuqUxJLJltuI4ILDWhkFQGt/K1h1qSXDH54lbNglq\np86s5XMjY+M8/Kial51cjtUVHa/axiLbtmst2O7CCo6Nip/dtptb9qi26+Tp+ZRX7b9tP6O2TFk3\ncnnsSdXUmCBgpKLrY2xvkemqzp83vPFtLNnI4ztvv4f1lY7thy5u7tK1zD34ee3zxw8e4Xc/8AcA\nHH/2MIm1XJXyPvGMrrnyeJFKWedtJ+rwXEfnz3rBZbs1s55udjhm86xVvDzmhJqSf/HnfpV77rwL\ngO/4rrdQnbWauLybWrEkcTZZ8/o85tx1/c6HKxvNFwQcPqmLan7pDMuLOiEef/gRHn1Qiy+uLy3w\nLfe8EgCnUGB8XFXCW2Zn0zQGcWJwM6r/OMP8e/B9P90gEpNk6m9lopWQTdFBeRsect99X+ID/+uD\nALzxW9/IP/3JnwJgvJch9gLYOjfL5JQykXwpl0p9ywsraSK/OIjS5Jyf+/S97LD1tR5/+HEOH9Ko\nrXJlhGcPq/r2k3/9CVq2GOfaSo1qRQWxW/e/mFtu1ai2E8fnWbXJ27ZtH6Vgmewv/9qvsXunqtiX\nlto89IgmgbvzrlewVtckZ52gxbyt93dRGLPZ9p3mXkvIzMG+4Gv6flWO6zI1o+aEsZHRlCHEcZdj\nJ9QUcWppmS1bdFFv1FbTOmHFUjE1wXiul2ZWK1dGmLBh4Hff9Urm549qP60v4qYm30tLb5B0moiN\nKsH1MD1TqhuDVYVjkoww5aZJ98SlX8vPBLzmdbqQf+e3P4hjN8knjm5waMlGmYXrHH5KTSOe5zA+\nonNwy0SRgydrtvUObRt9NF11KfeS+nmSRkrmHJdmZ3BhqtVqbyp0nI3ay5rVskVms9f70XZ+xgcq\nSc0JmwQfY4jj3vXNPlmXkpzzUgUy43q07USJCkUcmwXaK+bxc9YPy/dw7fi6ntf3z0oceuUTc3lh\n7w17U1rmT6qvSLPR4PobtNDxkfkzHLEmP4Ob+lqEnYTxSV2vrU6X3kuTItSsUNx1Y8ZtxNHJuMXO\nyZGB6BufmKZc1vnY7bToRrrJ570q+VHlm2cWl6i1dJOZ27ad7TvUVHfgsfvp1my1hdIkBeuOMDqV\nIziugkBlJGF00ro+rI5QcHuVFBq0W7rRjVX81L7h5YrcdKO6KdRW51m37w8iIef2BZN6a3BzNIlD\nGPfThaSR306SpnMwmbnmeR6ePQAEEfj2QNptNilZU1Fsciyc6iUXNTRsgeqFxUX2bNWDzcj4OBsb\n6guWzxdSgd7PebSsuVByOdod7Vvfd5my5sso6rKyujgwifHMqziV09/N776FXEt58eyWUfLWt7Y2\nf5yWNVMnTo6gq/vovpfs4zX7NaruMx/5MA89o8++anKGsmPTdnij1Ns6N59+8CsU9mlCy3rXwY+V\nRh8YsUW4MXn8nO4fSwurzFv/uziAMUtjEvk0g8F9ibMQgZadk/d/6St89ctaQ7Dk+oyNKv+bmM1h\n3UfZCEKcnguLyRPbtTUfB+Rtmo1TQULdbjqFJGLc8uktYYQ88jgAo29+NS9/mR68xevzHsfZ7D7w\nfCs0DM18QwwxxBBDDDHEEJeBK6qZOn3mNL/5278FwMHjR+jaE0FzbYPQqksJu3zpPq1hd3Jphelp\n1VK87nWv4zWvUom6nCts0jT1ql1nT8xZFV0Qh5lTrfS9kQVO2qroBw48nVb9/vhf/CWL1uE0TmJw\nBj8R33nXbQSBSt1Hjx1jbUW1Cxur67TqeuLzCgFbp1UbFZmAtk28+ad//Cf41jH2FffcwzOHNPLu\nlptvYmTUJgcxDq6N5Ai6ETk7gi+5/UbynjVFbJticlZPGY12i68+qAkfT86vU66qmrNYLPDP/9nP\nAPBP//lP4uUGTaRnUkdqMaavncHgWPOW4+VSrZDv5ilbJ8fEcSja6KNGvU7JBhQUCnMcO/4UAMun\nTqYJWeNuPdXI+Z7HhK1NNTU1TXV9pNcactacUC6NMWKTnm7UF9M8UJvr+g1AYbcDro0wcd2+NsHz\nEJtoVEzG0dpx0kAA43RwjI5zREJnXefXqBeR2DlbKo0wmlOtnEvMl+5V2keKPjdtVe3JaiNJU6gl\nUURoS3l8+8tmuGWXagjCIMBYjU87EvLOpajdN3fIuSJcAc6V78nNONeGYUBaJxGQngO36/RNooa0\nbJPj5DY5uPc0w9lovqz2+HIQGqEV6e/myhWk0tNyFsjn+sEDPf7h+R6u/V3fSKqZEomp2qCS0ZER\njth1WR4ZoWi1XUmSpGaDUr5I3Xavm8sxPq6Rm91Gja1bVAPhIHTa2p4gahH06oWWq5QGdND23Rwd\nqxk5+PSz5HLKIxZPn2S7dWjevWMXiY1ou+nmChW7/nfvvYvjR1RbEbqFVANZLOSZ3apjvnKqiYnU\nIVu6PqM7lY6N1jojNr+P65WQrq7jmZEqS7ZWYWs1Im9LvzRqAY1A13E76oA7eNLO2CN1LnfEwYl7\nLgZ9J42JySqjdnw6zTa+r2Pi50tpZN/KyjIrNgHm6lqN2Ibl+uUxFm1Uc6t+kpyj83FhbYliLyGn\nKzSb2n7jJjS7+rlrEnzLN9uNjTS8xkkSRioXt2L0sGeuixgNosmbkK6xecZaLWIbi5APG4yOjqY0\n5q7X8Z3duY14TfeqmeoIr96hGtQorPPw4+rGUXFdxudUOzo1uouvPWejuI8do2id0RutOp0TBwC4\n5eb9jI3oWI+OVjj4uDUpriwwvltdcCJ/hGRm28A0Qp+XLCws8ocf+kMAHvzKw8zZXGCrtYADz6iJ\n8/RSh2JJ21arrfO2b9uj7fF9brFrpesYLfsDOBhKlsdscVxutXNs2nPJWYtA+5mniVa1Vm5pbiYt\nRySmLy0kZIJxLom6KyxMbduylbd/65sA+ItP/G+OHFdVZdHzScatCjMKWbP1nR594mGKtobWM888\ny/xJjfh62R13s3v3bgBKlpkBuG6faUdhlApHruunpkBXwCYhRgRyVnj55Bc+yTPPqomt1Y4ZG1ch\nbuee3Xg2gmQQPProI/jWL6y20aK+oUzq9lteykjFLvg45O9+zzsBuPmGmxizflLT09P09ql2N8Lx\nrVnLgd7m1wkCFhfr9v1t9t2kkRauY1hb0WjBz913P4srytROHD2KsVFejuSpbejCOHKkid+Lwpmb\no9boFa28MDRRQJo4Is107vseEzY6MFcos24jF7vNNg5q0swXiywt6GKJow5ztt5gpTzBFrswc4Ua\nZWvaC4ImjV7B5iiiXtM2jo6O0W731OstOr0aat0gFabkNGm2ZN3sL0GaCsM0eaMkrvpEoZF9zqbw\nXhu+77g41tfG0CSuax87Dhw/rnOqVHbpRro8v/0tN/HiA5rR9+HDqxxfU+acc4TFms6X02ttoqiX\nioBUaPXE4a0v3Q1AM/YwcS99QoKbH3yeDsIqRCQVcHrVB5Suvo+V7/u4bsG+UYhsFmiDEFv/Dc8T\nehkNjDGpOV5EKNooUsdxUsEqDMM0sjaLS60D2ExyuNbcXSxO4NjNsZT3ybl9U2PPZcCTfjoHF8H1\ne+Mekrc8YNeuHUzYwuH5cpXnnjsKwMKZ03S71hRRGQeb9qBcqRBagW5po0MDpTeJnLQaQdzpUvX1\n2d3jo1T8wYSNCMOGNaXNnz7D7DZdQ8eCZzizogL99u0V3vz2l9r2tli1dfFir8zkNvUF3Vg5QmjT\nbTTrCatrunYr0w5+qALa2kabxSU1bxa8Mv6krZnZcchZH6WV7hIPHVRB0ykVWF7UQ+KZ1TbGHk62\nz23npC3IPhCNYZya9Au5fOqXaeKEsvULGylX0sNbGMfYpYjn54jT9CU5FpeVrqXVVbqeztlSvkqx\npDwjaC1zZlnNXju2b6NiBaIoilnt1RwMQzp2LRQcIQh0vidxV31zUX+i2vrKwDTmGwfS1BS5coFx\na45sLCymB9fi+CQFu1aSkRae9TetzT/J8Zrec+rEabZYXrgkCQvW/7LQ6XDXjrcCcPcb3op89PcB\nGN0xStPonhS2O3RsPcfVlXWCbs9E36YbqjvIzNwW9t+m6X0Sx6MyOj4wjYYk5ZGLizU+95n7ACgX\nSjhWsM15uo8BtJtdJLDCTpywsLRs73HZZfu5WM6zFCntBSdhxArduws5Jm0kbsHRZwAWPv8FPt3S\nOXnb97yDbXdpomg8J+MXK2RKBW8y+V0MQzPfEEMMMcQQQwwxxGXgimqm8vk83/ra1wNw0/X7+Nz9\nXwTg2SPPsb6up4YkSdio6emg2W4xNaUaoiSET376MwDMn1jgTptzY9++fVTs6dMVrfwOqocoFvt5\nJAIr5foiiDWNGBI+f9/9ADx+8GnaNhW/QwGxEXDl6ihrNT3lzRYvfvKfnZlmfl5VtiYS/vGP/IS2\n87ob2X+rRmzs2LGV9d4JMehQ9PW9QbebOvDiQGxPupHnEHRVK/Pvfu0/YLCRFq9/Pdddp2aDRELW\n1xYAePLAY6xZk+Lq2hqTViNWLRfoLOnv1tbX+LOP/ikAd9xxB+/5gfdclDZsr/WqdovTdwpOSNIT\nm7hF8nltY7fVpWNNdWHQIe5pixyH9bpeL5YiJid0DD0voODpPZ0OjNqyGCZjRjHGIbGn/VargzG2\nJlbUyeS6Mmn+ELkUrRSaqJU0F5mkWiERQ2KPLRIneDZiiygEm1SVKCRY0VOUl8uzZVrb/4Nv28sj\nz2nfP/Slp9h/nSYEnBkpsFjT9r/xFVv5iy/q6X+tYVLtWISDZ0+cIyMuXz2sWsf9103j5KxmNgzB\nHfxslMvnaLcGLz+TRZIkqVkoCAIcq3UoeA7PPKqBJNWxCaZ3WpNDnOB7X++8DqQaKGNMGjno+35q\nvspqxC4VQW4SJ6dzv1AcxbPOvHlX8CTrfJ/RRpFOoFTrijFpvh9MzLR1UnbzJSY39NQehwEVW53e\ncQXXnoZLxRKtrtUW1CPqjmp6IvpOzWF9jbyNlHvR9lFcZzC2fHJ5kdjm1slXSyRW42cSJ3W83n9z\nlZtu1lqhpxafIrC1H8fyLo4NDFkdDTjiKf/N5fYyWrC58So5xGqsxqdcTjyjZsGc7xNbjUy56FFf\nUe3rlx98ihWrZTVOntB6EAdhyPW7r7N0Q7lwCdG1kVoZAIrlUfbepM7W5UKeWk37fmFlgSCNam1R\ntB4RK7VWOk9rtRpLa3p/EAR0rIuJSXKMjOl4boRCs630lkuTFPLWJaG5Ttvm/mq1muTtvtJu19NE\nuTnPRewaldjQtRqQQSCmQNnmxnqlM5OWw3FLZTxP+aLkixR7tV1NzJFV3SMP185gLC+cnd3GfNtG\nek+XiZdUm3b4+BLug1oG5vu+7wfYs0MDknKVHI+dUg25Xy5RslpT3/cRG9V46uRxAhulWPTh4JPq\nMkKSUOhZ+9/5jovSaDDp3ibi0rCRrG4eGja3lwhsmVIawyCmYDWDSxur1OranzOuwbGK27wLY3Z+\nVnI+k3aNTjbb4Fn3gXwO12r4S92E2lfV2f2++dPc/I63AXDj219P3ib29AII08zM6X8GwhUVpiRT\n32vPnt1Mz+oknj81z1FbyPD4seM8fcgmfqvXuO56Dfvcvm0XdWvykSjhjE1Kl2CYnVXzUqVYSe3r\nxpiUoSWZcO8kilN13MlTJ/mEzQ7eiUBs+GvQDNPs7OLkOb2gk/Km2Yv7MnRaEV1r2vuRH/5xJqZV\n9f5f3vd+fuHn/iUAU1MTFO2C8SuVfpK2TBic5py0CUvFpdbRoTp8dJHbX6KFgO9++UuIrN037Hb4\nVz//iwBsNJuUyrqJTE5M4Fr1que7NK1vmhM7SGRNNU6V2JQuShuo0BRbEwaOk6aLjbohrbbNXusV\nmLTZj8crXhqqHCeCbwvXdbpdYmvmqNfXKJVtLbMonwpEI2NTjI7ogkqiLu2OCojNeoNgwma+9fME\ndsMNwk5ay8oRrRCYQgYXqPzqSH8NGcG4vfpPeaS3kk1CapPNCF/GJGAThyaJ4aYXqVq8ksvz3T//\nFgA+8rv/iX/9y38EwPVzZeJA3//et+7nR952KwC//UeP8buf0E0q7wtvuVPXyqv3b+dPv6DX/+hT\nR/iBN2o02Q27J3qZMQZCLpejawWWOI43sYw07YgxmbQBziZLaTbffc+YGkcN6msqDD75xAPc+Wql\na3rLjjSJpet6m1IR9MyFYRim9dVyuVwqWMHzF6jWpYSxwmYun08FupwjeI717zsrerGXiiNOor6p\nMYlZXNCDytLiAsWGbjqTs7M01lVwfvU9d1MY1Tl/75cepGjD6nESOjbqqVQdQzxl2kHsgTUjtqIJ\nfBvFmZudIxww+Wqr3UoLw3smYnFh3tLkUy4pfXfdvo0TRzSkPm4cZ7Kq10+t+jz2tApHreOnqVyn\n47BtcolGSwc6P1HEn7Emp8U6k+NK90ghz5JNBPzwiQO0bUqORgCxNfk26hFlW/Dz+vFpZm2S2tpG\nh5I17w+CbidITeuFfJ6X3qWH6IlKkS9+UQ/XaxuSmppXNlppck7PyeNbV4lKtcJIS3nJ2soyjnUT\n6NRr5HK9gr8Osc0avrbWoFy2tVE3VugEuvd4ORdsAWfXSdK0Cuvra6kfb7lUSl0VBqKxtsp6ovNl\nYTngZYG2YVfFJ1fSPvQKmeLJUuLWSPeiFVeojOoc+Fi8yBPHdEw3mg0SK7hvv+Mu5my6GVaPULa8\n1iwg8YIAACAASURBVF2GuVn1RXrw4QdZXdJ0AsVikdCuV8cIrk1PFPsr9FZ73s/jyuCJSTFCx45L\n2G1StgJps94msekrir7H5KwePhtxQn1V+3k8ESbt3lz0Y6So7TeFHMZGdwZxQt1Kd25kGIl0nrtd\nB5v4nqATpslUZf4MT/2ORuwvHTrMbe/9XgCm9+yB4BKiTTMYmvmGGGKIIYYYYoghLgNXVDPl0q/p\nlSQJo9b8NLr3RvZaNfDGxgafu0+d0/7wY3/Ok09ohMHC0jq3WHX1LbfsTU+QrVYrzfVhIqFoy8C4\nntd38CTepK3raTK+8rWvcWZJHQULxZG0NEqnsUYh19PUeHQ7g5tDVpbWuX6Pmje+93u+l+dOqrT/\n7ve+h6kZNVnmfD+N/hIkPfHHcZBWJ1etVK/WVpv/+r4PAPD973ove/boyc7NeThWdfqRP/gIH/3o\nn9n+uZW3vO0OANZW19J+mJqe4YnHntRnJZdqjHbt3ketPrg03kuGKfRLyDheTGJraEVxmyVb5dtx\n/TThXT5fpuTpmLtePxdPo1Hrl6gxQs6e6iemJhm3+XKa9VVWbbmGVqvFbKBJ3MZ8n3ZLTQtxlKT1\n/jw3n8mj5HBJsRmjU4Qr+lum3Uyj6vyxSdwJ1SzEnRaJTT7o+l7fOToKMTbfU+SGVOyJ8ObX/BCJ\nNS298c3fwfv+v78CIIxcCkX9gfWlGtvm9P0/9G27uX2PzsGi73HDDj1VV8s5Slbr8CefPM5nD6hm\n5Ke//Wa+49W7ByYxa1ZzHCd1wnYxJNI3lS8t2Lxwzx1Ka0jmiiVcq2GMjJPWS4sai5x4TpMAzsxu\nJWyoWSXn7sS1kaZxHJ2zpMymvHCZiELP8zZFD16KQ+iGUyaxjrqe55HrOZo7mtMLVFOWLY3T76BM\nmYkkYaNWs/e76TxvrK/gW9pf/aqX89gB1RiasItr57y4fnoyLlRLYCNxvTBBfP08MbqXMVu7Lhwb\nIVMc7IIoF0ssWA19dWyGkQmlqTLWZMusnt4n92zj1NNfAOCrj3+FGRtYc+pImT/40BMA3HZDnpvK\n2t6xXJFwQzWE9z/9VBoNOTKSZ5edgw9/6TDGWNeKCql52fdJTT9TY6NsBPqek6e6jFSt9i+uMFEd\nG4g+gLWNNao2YWq9uc6jT6iZaXasSmC1RcWiR90mRo3CKNWqjI36FAs6x8ulPFOT+rud1nZWbbDO\niaUGgU3qu33LLLNW+5YkEQu2TFWrs0yxZLV1eY+uDbKYnpqkVNJxO3SontYFLZYN+fzgkbXrnSZB\npPPrRDvh0wv6eV/O5y7rbrKvmGcyr79VKpaZtHxuu+tRbijvXl5dZd1aHp4KQta3q1VEZrciFaVr\nsd1iYrsGLdVOrLHHRprmb6ixaCMip0arnLb74rOLiwS9epVRlBYKC5OYdjRY2SNQU3pgS+90W0ts\nm9T2HDx0BmPX6FqrS84mZu7GBndZefBOEzLWyzHpe32LQyZKu5ZAbDVWcU5oWjV9ITaULT+pGAfs\nPDFel8p12g/tvMd99+u82ps43LxLIyWTSyx9dUWFKcPmxJr9ZAVC0UYi5aemea1NgbCyscGnP/NZ\nABbPnGG7jVYJWq00THRybk4jPoBGrZ36Wvi5HFUrrDXadbrW7iuuwxMHVUB74GtfZb2hE7cyNoZj\nF0O3VWN8TDfxarlKuTiYCQwgShJ22Yzp5coID9yvhW5LlRGM3SwCA27Si87qbxTGzacmP0+EmvXV\neujBR1myYb2Li6d5ye2aVdt3XY4f083uQx/6UBr23Gg2qTf02fJIlXLZZrzthtRt9M+eXbPc9fKX\nKe3lEYJLSPjYU/UaSAUEJMGxvk4JCVEv6saAdf2iGbnUO7ZAZ2GUYkEFh2ppNBMJI6l5JYqC/5+9\nNw227Lru+357n+GO77759TwDaAAEQGIgCIKTJpKSIlESLWsw4yGuSuKqWImdVDlVKVelUklcyadU\nJa5IlcRxJVISuURblEKLNCWS4gABHAAQYzcajZ779fCm+959dzjT3vmw19n3PhhC3xYclFI5/w/k\n64tzzz17nz2svf5r/RemlCLQ2hXvBLIsJxNDJstSlGSAxHFETTb5OGqQyeS1BFNvUAD52jVYdXRV\n1h/4VO6iP6AuMV9mOMTIeNHxQXSp5ZlmGDHuiGukuUg7GIsO3TuxDYWW50nyAeduusn+7Gur/NKS\ne4eRbvCBI26hu7GdUkh8i1EhKxI70Qxho+e++49+71UOH3B9+zNTtNEUhrpkDU3Ki2C1N3KVGfCd\nHzga/OVn/5SoHJtRjcyW1LRmJP0chJqnPuoKVn/8Mz9HISK3Ua3pM1yTJGFYih5OyB8opfzzWGt9\nrIsxe5/z7dIN7wbVWaAWS5ZUGFKXlxQAVo2z+UpMFkFXWvmafYUxnhIdDodE5aqpFcckq7g/SLh1\n01FftThyRflwav1KvlALYi8fYgtDngpFF8dYyVq+NdKoCYrz3ZAkQ5pCl8zNL7MrIpPLBzqozI21\nt956lgefclTO6Y8aGjVHC5/5zhoLLTemdgfXWDruaOStNzUmd+MotkfJcMb64586gRaKcpg1GQoF\ns90dYEsZlFAj3U0RBuxfdjGii7O32d1N5Zm3GbI4VfsAcjKQzbbb3+D1c06AMT92iNjr6ioGG86I\naMZ11nvOUOrZjEjimKIoIpZsuNlO0x8kukPDRldESpsh853S+I4ZJG7NbQTWZ1gGWtHM3butNy06\ndGN/abnJQOKq2p3Ix09Ng0xFWOm4oFZnrefWlfUr1/m+zPv9YcypfU7w+PiBFoekKPHhdMRhMfrv\niWv8e8tuHOXFgA3t+uTs6htszrvvbr5xiesjZ3xdffllNnbdmKnPzvDgfW5fOfLQA+zruD3j8rnX\n+dbX/wSAN3ZzUjEea1qh1fT1XAtlCWquD+cWZ/nQh1z4ztbODrsSbtIfWgayH0dBSLPh+qQ1GiJD\nlW6We+khGwc+HjYtLDXpqzSK6MleFKYW5BC4nBjm5JCz9PhDrHzupwAYLS7w4rPuYHHmD77Kyb/j\n4odrYTCOYZ4CFc1XoUKFChUqVKjwHvA+e6bsROmJvaUkTElvWeOD0z7zqU/yitTsu7a2RkvonyMH\nDjIj0fdaKZ9FZgt8EOvq6jXaUoOoMzfDbt9Z4BeuXOZ7P/wBAJvbGz7TrNFsooW+Guys0zzuskZW\n5ueZvQvPVJJnHDvpToIGxY996icAJ2Zo5AS30x8RlkKgxbhEglGGvng1trs9rkmQZ54XfPgjrqZe\nr7fJAalavnbrFv/i91zl97Xb6/zVX/1rAJw9e9bX44vCGrlkOcRxk5Mn3cl0e2uL73zbZVN+4ic/\nzf79R6dqn2VcTqawuS+dgjIgpzFjjf/cqglfrFVeZK0YZQwkQ7E/2mJhvqwMv0BNgknDSHtNq1pc\nY3nFnZ77/T47kvEZ1+ueslFK+3p/YdBAIx4iFaLvgubrnT3HD151nqnd3RGnhd44kIwwkoWZJyO0\neFPDuUWsZIoVWUohXgxlLdx2NK86vs2b3/hnAIx6t/g7f8OVNbjdzXhD6KGnHz2MNe67qxs7/O7X\nLwGQFfAbv+jKdGgKVOTaUq9FvozN7Z7hKy84L8I0nilrDZNOnrLcS24UoXj6it5twqE7nZ86MEdc\nLzMuA3LRhEqThN2+BJDO7eMDjzpNI1OfY1CI52UwolkbU3jlvM/zfA/tX3qt4wkB2SzLvJeqVqvd\nldZUo930elJxEFCXcWWNq8nm2l2My0tNeMoCHRCKtzTSilCCxfv9vtcva6NR4lm+dmWVnpTIqDXb\nvhyRAZ8ZrKKYWM6vOoIiKuuUQq7Kkks18inHapqmxJIJbE1BMpQ50Vgmxf3mN//liKcLR70m9XUO\nHHHU6+L8x9jJnJcn0TGheAcO7m/xxI877+i//OJVdnK31qTbXQ6dch73c8NrbIke0/Jik0GZHxDE\nnJcSSLVGh9aM6MI1YEbK3mzHswTh9F6b/UszztOHrAeSNZuMRoTisUyTjFTqtdXiGWJZW4skoS/a\ndHEtcvVQgDDQjMRboU1CPuzJ9TMEkj0HI2LJiAyKAMQLbUxBKHvVaLs/HjuZRUmmpB1lEE2/3pja\nAqNtyQAmAwnC7iQJszc3pI27/EgCxM+2Ohx/6mMANNIBK1su6PxAZpgTPayHegOerjkP3c/WYrak\nhuTNf36ernXv+qnMEsva+XrvNpdXHa35m6+9xm/8XZeF/qu/9jf51IIbA89++Yt8s+/ueS4LyMPp\nxVetsZ7GP37yPmakdM2BEye5ccuNyee/9zIX33BjVTVibglVl1nNonRnloOXoMsUVhKC8iAnFRYj\nzC078t1hYihkzdjKcpb3Oar34Kc+ykDqwQ5W19CSVHXfwaPcvumSTQ4e3j/e36bA+1vomLF6slIK\nW6byWpwlhHO1t5pl3acmTRHVDGzB2g33soskQxIVGA77PtsnUJoZkUnYCNd5+SVXh25zZ539R1yc\n0aVrV7m55oyUerOGks06ChSJTLy0v82SxLrUQ83QC1ou3bF9rdk2l0VV/dqNG7zykluwwnqdoO66\nO88zn7Ez2EnY2nQDdHuwye6u26zTJPf05fr6JrsyiD/1yaf48pf/bwDOvPYqG5L+ioUFkZFYWFxj\nW9qy1e2ysODc6lmWc999zr36pd//IiPZEOtxyIoU1JwG48LFhadLwKDKvy1Yo/zn5eZjfW+7mLBS\n0mCUdkkkvmJhLqXdcotJoxl7w00r5V3z7XaLXVEkTpIhSrkJFYZ1OqLcW6+3GUg6rdKhj8maBl/5\nzhn+8PtuQgVhwC/J54tzLcC9B20hlHFqi9yr6VpbeEbR9nsk15yIYe3kz9EXCYTvf/0l/v3/6j93\nzzYL3/2nvw3AtWs32JDF7Z/96UW+9qr7rc89sUJNZDkslgvX3Ltd3RhSkzEVaMW5q9tTtzFNsz21\n+cqC1aG1bInr/9r5VwjkjR06eZp99ztDSWMYSFr6jauXaQitcvDYvdBwhudOP/HLUGELikzotolM\nvSzL/NxVE+8X8AtvHMf+mtFotOeaOyEKLFEg2UdB6GliYxRZURa9tXuy+crfDbQiiEtKDh83Z4Hr\nN12cUrDVY0YOfhubOxQSg6ij2p5KDJQinDr0sWlRoNBSO83qgEKy8nRUIwyma+OJU8eZFWr/4uXL\n9AduDtXqLeYWJHNtfcQL33Vv4rGnOmytund76MA2/8k/+gIAX/3y1zn/J26jjmYT1rdc+8LaLjOh\nO2RZk7MlStudZcvtVfe8t9dHrO+4DX92ts7Ssotl/NCjT7Cz5gq7t6IaFy+6+bT/wGFe+tHFqdoH\nMFsPfN8rDEaK2Y52hyiJ5zK58e+tsBnNhrwHG/nwjjRLiKRCgNEFRg4PoRmiM5FuGQ3IRQTSmoRU\nspNVnnilZ4P2MhnBhNFrCosWeYOsV7isvymRh/Okxr2XYdL34SCtYw9w7KAb+4evXaK+5fp5ePE1\nAsnIq/3ET9L8oMvu3h2l7Pbc2rBx6TzPS8fNrV1jX8/tEweinNPl4bMdk4pswMkRbEoW8sPNkEdk\nyG69+EOWTrkY4J//63+bJ2+6Pfi7t27yzBtvTN1GbSwUJYVeY/mIM8yfXlri7EsvAtC9cYmddUdT\n3tjM6IqhtBEa9ktoSV1b0vJdFCmZ3LOfDskla31XBWyJwdg3lposyKlVPrzGpoqhhMJkxjLbcfvf\n4eMP8c1vPQfAL/7SZ2k2p8/KrGi+ChUqVKhQoUKF94D3l+ab0HuaFO+z1vqgY431Ynntzhy//mu/\nBsDV69f5/vddxH1vd0BdAjaTJCMTD05WJNTE9Xvk0BFmRGvp7MXX+db3XYbgTq9PLhojgYVYsiIM\nTr8IYLQz8JkHtzZuQF6eMk7csY0myNlJ3InmrUsX+O3/3Un3h3GLq7ek2nx/2wdEKzMOjs5s4jMK\nkzT34n1PfPhJNiRrx5qMgYicLS3Ms9sVF3VhSMXT1JiZYV3cvffeey9zs861eXvtNq+86Prw1q2r\nfP5XfgWAg4cP+kyUO8NiSmUhlfssAjVOShRPo/SHGes9WWuxQqVqbSi/rIylL563Ijc+wDAMQ9pN\n9w61hVR0prI8IS8zB7MMIx4EpQKfbaJRBEGZrRai7uLc8CcvbnJ5Tag6ZfnheXcifOSeJVZiuX+7\nQ/2Io4JVveXr0CmL9yz0NzdZv+ECRe9/MuWhH/8bAHRvXuG//y/+SwCOHjzAb/5fbmy+/NaAhpT4\n2RzmPHWP87L9jc+cIJSssSK3XO8Kr6KVp4i1guu3pxcKNKbwc9HljYpLPRlwS06fcysHvFdx5eAx\nOvsdRWyLhMWD4tmpz2Ike2pmdpmeCIG2o8z7IS2KXDxfRVF471IQBN5T/fYSMuWzRVFES0pGTVJ+\n0yBUzlsNgFK+FEkQBdSEBsvzfI9nKpbA8TAM0aVOnUmpSy2/UZr5siQL+xtoafutbp9EKL+gEXm9\nOwrQpTdea0K5ZzBRgkhp7dc8FYY+EeZOeOKxDyMOCk6ePMqzQht+/wfP8XD7EQBOHDvEKy84nam5\n9iEe+JDzpj7/zVdZu9d56H/+F/fxT1+SOn3dmAs/dF6kpjpJLHX6Vjd26V+8BMD+fXPMzrjfakd1\nn4QS1CI+8LCbEw88cJrffsYJIj94/1FW1906te/ILK2Z6cseGZP6eexKEbl3uL2zRX8gdfGSjKEk\n0PSHKTMyXhpxTLbj5kochdRq8p5NTk2KmmadGv1t0eFTKSMR22w0YkLJUtQq9Pp1SVH40jUB2tPO\nOgx9hnluMu/5mgapniOLXYB4EBu00NE3i4Q/i0XM9b4TfHDTMSMHb94gOOMYj1qkqH/a6dd1Hv8w\nmQiTZvfdy0geurfVY0u8x69dfYuGJO+sdLdYkqzD+kLM7Mi9o49v3UT/1v8EQPHoIxQPOa08Vo6w\nr+PYj1+8z3KPvTNTU8Kyl0bPhZILoianTrt6f6GGecmWvnljk55k9nW3t0kG7tneutYlFF0zHUTU\nRM9r5Z4TnBctuLSwZKH8Vj1iJN7GVCnmyrqvWmOCkkKISeWa/+V//T8Jcff/hV/4Of7yinZOFDBV\naqxLbZkIq8FlxAEUVnHPPS5W5N57TlOPHLcXRpHnQcMo8kKX2SAnk8VWa0Vd4jSOHjtE+IL73W53\nCyuprcmgTyibdRgoBhLzEDdmuSJc9bM/eIFHH/rg1G2cmWuwIfy3IefwIUcvPvr40/zX/+1/A8C1\nq+cxRVkvb6zmvUdxVYfs3+9c5p/6+Md4qeMGwdLiPJEMgma9zup1Ed7rD+l2y7pSR/0mtdPr8cwz\nbrO+cfUqo76bbKs3LnP5suOnH3/q6el9lIo98VBlJh0oL+tg/f84SsWY0miemFAT2XXKqHHK7WhE\nX7l2bMctKHlwpSlXNK2tj7HLs5S8EGVrFJub69IfPS9WqnWImn5OcO5Wj5KlbEYB++fGm3mv797D\n4v4Zov2HKRtmxdCzShEI/bc9LLi26vr7ftNDz7hsmaf/+j/EfumfAPCNr36Xlba758MnGpw+IOnJ\nCx0+/0m3wB6Y65CI8djdGXHumshOaD1Zs5v+6C6ESaPIGxHGGK+23k8SZhfdIjk7O4eV9H0TNRkN\nRT7D5OTiap+ZmfdqxgMT0mmVtecmjBS9t3Bx6ueo9vFR9Xrdx0zl+VgwsygKT+GEYehr+U0DpTSI\ngWN06KwrQIfjenJxHO2RXigX5zCqYXWZ8RewvOwOJHFrhsJIdl6tSSAZRzupwUimUBwakDFfYyxU\nbK0lDMcCvaWxpvQ4oi/QlmBKhkgHlpFQe81ajcV5N05brRkefNDJyGxurnG77wz6l8/PcP22ZIfN\nazZxY/MPLqzxyMfcJvnDP0rZXXWHRt22LK5IIeUvZ9SMHNy2UhYlo/SV8zc5dtj12U/9zE+ytSN1\n0G5c5AP3uXvOz7Q4sOJoxxubKVevX52ugTgJizImMoojL4yJNqQyJ3q7fdY3XFvmlhZoNsrsQkOW\nuWc2dqKGpDI+E3BpoUk9dmt0bgzKuA08z60X+o2i0B9a2vWaP5iZwvixo7WaqC2Z3mXNBYUS414H\nIZQFy5N1tvpuH/puUOO87FUPnTjK/SJdcPTWLTrnHd3WOnwMMyeK6SsHsF3XJ1uXztF5zFH09snH\nSIRS7p5/ixurQrkay7zsHytXL7Asyuizt6/BeZf9Hnzq0+gPfxSAne8+j311+vf49soHQXm4wtIU\npfOT9z/C3Ipb8/rbW1w57/anKxcucP26cyZcNCNuy9za3R5x/xEXwjJz+CCZyJfYArTE5tZmmvQl\ny3L24DKf/Ss/7/5enKWbuLmjCT3tf/HiG9wjIuC9jW0ajeWp21jRfBUqVKhQoUKFCu8B76tnyhSW\nPck4vlaP9u51i/W1mEbDcYCqDjRPfNjpInW3emxuOqs7DENv8dbrDTI5rQyGPbrb7kT27A//jJHo\nVwS2YDCUunijIVbc9LmyPrvp/kee5IlPftpdo0LCKau4A0T1gESqb6+v3+KB+13w3isv/cjXE3zi\n8YeYabt73rp50weaN5odYskQ21jfoi/1o57//g/Y2XVtWVxcYFtKWGDg5Zedu/fQ4YMcOeaCRXu9\nXb77zDMA3L55y9cu/IVf/Hm2N51r/7d+802+/o2vAfDwox+h0Sot8ON3aOGkl0Ht+dz/2+K9VMpq\nlP878CUI1ER+XRgGvrwHBL7M3Wg4oPTEmqKgJplLcRw5jySA1p42StMR29tCFxZm7I1SE7zjFGhH\nAd2+eyePnZ7l4WPuRLi9nXL+uhs7T801aUl9MlToEygUheMkcXRYJvScKQpfFkM35vjJL7jg3x//\n2Q+xc/YsAINr15hZdvpmYazJBqJ71t/EZpJNNspJ05I+sz6wXitLUtaPmwJ5UVBM0GrlXGy2Z8fZ\ndsYyO+dOfsZqbNmhQegTDJozS8QtCQRmHMA96YV+u6fc13M0xs/vycy+KIr8NUVR+BN/mqZ7vEh3\ngkVjKb1jodeNCsIQW/abVj5rT6vx30EYY8t3h/HJHUlmfFWPIBjfs95oEYse2Sg3jr4HFIFPonDP\nIV4NGxKJF6zUWHP3DAjj6dabV86c57B4zK698jrf+973ADj94CO0O24cXb1xG61FZ667xZlLbh25\n0Oxw7Jjr+wcOD3ntlpRs2d/hXgk4PnJEs3TCBWc/88wqf+WnXQbZCy9e4MIVd5+trT6zQtvdXL1N\nd9c9+yc+9XGCnqOL49oMnaYkFDTWaTWmP8NH8TgAPQiUp9hQ1s/vJE2oSaLS4nwbLbRjFCislH7Z\n3Oiyf8n1VS0KMOKB0jpkRkr57PT6hGFJ/6aeWqp1Gp6eJRhT61Et8p8bY9BWrsnvTlw2sAkmcWEZ\nqcnQI+d1ChuaWMaIAW6Ll+o7Ct6QZ76HkIdecQHcB8+8zuy9zvvdPnmK1oOO1Vk5dIjmighRmoJE\nNKr0zgb7PumyzfNkRCpB87ffusDl606/MP/2tzjwslufVs6dYfm7fwpAr1djY3V6z9Tbs3BLXTtj\nC/J8rBlYMhZpVvgEkOZMnQP73Rh+unaYV8675796bYszO269r71VsNNzXtQ8N75kjgpA4daPI6eO\ncPQBF6rQ626A1FXsp322Ll5wfTgcclI8g/lom0TduYRciffVmMrzCcFOrfZkWJV16NJ0rAJeqzWo\niXFhjPELXZYWbG66wVcUmaeRarWGL8IbBPDSSy4+6M++8y1Of8DFEHRaHZ7/MzfJt9fXaIjsgc0z\nVlacu/czP/d5jt3veOKZWsTSzPQR/QcW5uhvuWdYu3qDxx9x7tVDR47xf4iMwYEDh/nsZ52xNjvb\nYluKPK+v7zA76wbNCy+8wB9/7asAbG5f5z/6jd8AYGtrk0C7Pnnp5Zd445zLmHniyY/SkIl39cpb\nvPKio/aSwZCOqIj/1v/4luf1Dxw6yT33OlG90UBz/pzLOuNnPnrHNiqfXjMRKKUmjCmlvStcq4Co\njAch9pNocrMNg9AvYgpFJsa0yccGlI0soWxWKMabYRj4cdQf9LxKutvAyqwPO85AnAK//IlDvHrZ\nGd8fe2CRWam/9caNEa9dcAvaRx49id50rueiUNjcTWQzGLB902VG3b6xyc1bkim0u0bccfEkobHk\nYowU8Tz1445WidotgiW36OWDHsV152qnF/iU8MEw9X0eKuUpV2UtjeguDI0JiYI90HvvUdpBGijZ\naG1DdBmPZiHysVHv3MeO9n1nY3ZSJsHHTWq9p37fO8knTAMVRL49VgUYGZO5YWzgE/hqBMGECrsK\nAlRZNN3C5pYbD9tJwtKyoyLm5hZY67o4DWsUSmJswjAikEw9RTCRNanGMTYqJBRaMIjCcfZiFOMV\nYO+AHz33HEPZMJfnZjl41I2vN86+SiJFxOeWFjhxUqpLrF9nNJJ4y8V5XnrBzZUzr+zy8MNu3Zlt\nDzlzRlLVf5Sz/6Cbc48/usSFy+76TnuFowfcM25sGJ543GUIN9oRTz394wD8z//k95mTeJxPfORD\nNFtuszp37lU001db0HpySJo9IorlfpLnBcvLjppWKgebyd+afUvu8zS5SXfLrbMri3PkVt6Jtv79\nWFVQq7v2pv2hN4LdMidFqdN8HJJijS/jZrETMYj2bkqBUhSKuOHiI4vhbYrEPafJAtqS0dbQGi2G\nZM1aAgkruFbXbEh81sJ6Qu2cM3xmooB9EiZy+GM/xqGGo4A77TZtkbjY/bNvMiMUWxIHtMu4s61N\n5n7h8+7+b13kta9/A4DvzzVZuvJ1APbVZnirfTeUOxNjX3lhYB0E1JSbB9oa/y6Gg12vLj83N4eV\nOC9l4cET7oAXKstaV8R0k4E7yCJFp+XdZcmIMnzt6vV1fu/3/xUAR04cZLEhYSCrt4iFUnzqvmN8\n4gu/DsD8wYMUd3FArWi+ChUqVKhQoUKF94D31TOVJrmvd9VsNL27FCypmPj9wcB7o2qx3kMpdAAA\nRQAAIABJREFUlYGrcRz6QM48L7y7N88yf/3Kyj4+8+mfBmB3Z5tHH3eilwtL+0hF1+lrX/6X5KI4\nV+Q5h4469/bRYydoiQhjPYS5mVLI7c6IbUxNLOo/+crXObZyHID7HnqQv/f3/wMAvvfci+z2XVs6\nszM+280UlqEEwX/4iUf5/OddlobShkhOKJcvXeQl0c/651/8os86PHvmDAcOu9+an2+QCpVpjSWV\n7MV773+YDz3uqNIjR46zMO8s/CCIiKb0akyEmDuviJ38L3La0KEXLQxURGQluFLHPrNp8nQSqNAH\niysFgQQ3YxWhBECHceSD1MNAo+OyHlg0pvmyhJ5oJDkxRvFM2eKuxB6tsSy0a3IfxdqOG6fXbu0Q\ni67TC69eZl2CQBfaDYbFOBv1ynXnNb3Vzej2hApMt0CJuKy2aDkZW63JhR4I9h0kWnCnSb15k6I8\neRc5ucyV29sZ25LNYpX12Z+F1RxcmN6D+udF5NsJTm4yHwLwlKuyyrvjLWrskJRPAP9cfxEYY/wp\nfzLbbtJjNQ2sVaW0DYHVPmkBA8jzhXFIKPU8wzDwtF0YaXRUBo5HjFLJUtQ1PvCg81ofO3mIbz7j\nTupxrU4ugb0Bkfe0BkG8h9YsoXREKF7XIAzwcycKvSf3TmgEGa++6iiexz/0KLdEIHbj1k2eesJ5\nmDutiO1NKeOx7wQL885TM9OuMZKs4/MX+rz4ilvjPvrEIkr64Mbta5x5XbLDRrs8/ojLuvrJjz9A\nLMHcUfE1bm24di0dPcZ/9z84zbTvffd5PvExt+ZaLIOumysb2xlWTa8V5sjjkvLN/HIThiGphEeE\nUeBpPuzIZ5GmSUqz6Twyhw8dYl3EGHtxxOySyxpLsxQjdG4QR4QSbqJHCY1SR27C6wSWQv5Os9R7\ni7QOxtmoFqLwnefXO8EWGlV63ZM+Rsot5YVlUNJhWtMo51SgUbJ1R6rBcJ9bx28dbBPVXLvWFVyS\nAOsX33yNpVWXqNQINad+zHkPg/U1RhKA3u50mJHEk/TGKjXJjDvw6GPMfNV5c8xOQl8e4a2ky6gx\nvfmglEbrMqxnzGgEKJDxXqu30SK6XGvP0JlztGxnbpamhKrcXl2lEA0pbRQLDbfer+1k1ER8Msvt\nWLjVGAopMXbx8ioX33Le/k8/+TDzc+797ly7Rdp0/f/Y3/p1jjz+hGtvlvmyX9PgfTWmtrrbxFFZ\neLLl3bS7u7t7UqHjUiyPgl0R1pqs0dVq130WWZYlfrEd5ik1mQydmf0cOOAok30LyzTEwGk0O7zx\nYaeq/o0/+hqFxJ+QZfS33cJx7szr/NRPuLo9oSqI7oL//pnP/io13Iv/e1/9j/lP/7N/CMDBo4dY\nlCyBfj/l9On75RsZZRjAYGfE+pobxBubt7m9Jpl6gx6znbI4aMDFi24CDPp9ylG5ubHGogy+ZiOi\n0XRu45Mn7+HhR9zgOH78XtqiHF+r1bwB1WjWWVycn6p9FihT3Vy2lEwQlFeD1ipAywQJVESEpJsH\n8YTRNE4rdqnh5T3x/W2MJZU6Us3WDDWJJQnCgFCMqUa9Tii1z5LRCCO7p7UuLsj9re/KmPr6y+v0\npbj1K5e2vLGQ5Rodus/PXOqhxboIo5BPPej6/uhSk5cuuk3qwo1tlBhKWzcuMHPSbXDGFlgj2X8m\nJWzJu42boNwYLwAt76fICr9xDPPcq+drq5ByVBSF4uFj0xv9fx7tNs6xLa8rP4dg8sN3Cpub+MDe\nDc/xLnDp8OMsP/XnGIHvBB2MMxZ1EHr5gSAI/AatoxhdxkyFwVidU+MzhpXS3shsNGZYXHLzuNvt\nURdDbN9Kh41tUcdXYCnHeeCf2amqlxIItYkqBeNiy2hNNGV83xBIJJ5sY2Od82+8DkC7OUNP4i0j\nBYf2O1rS2oST0bx817Cx6WqdvvHmeTal9mejcw9vXXCHr+e+s87997uN+r7TS4xKhfr6IlqqF/zU\nT3yOr3/DbbZ/+C++zfM/cDIMeZZR5FIjb/M8dSk43K7FrG6PpmofiPC4T/seh4bkBgo5gDebIUpL\nLUebeypQK+0pxVarBisu3CHNMtJS8HPiMJCMRuxs78h3c6wu55wZx0QG4+ujMPAOgTAMiGUdStN0\nLGA8VRsLCslaxoAREWIbZiRFmVFqSGW9GWpNMOvW8fb8Ig0RZe7okCIqD1QBUdtVjNi5cYOmxArt\n7A7YvuLiodZefY1DYjAuxDUWjzu6Ntjtc+C2i62dO3WSTQl/YXOTtjznoVgzuovQiT0xlIzPcgo1\nXoqU8vtDs94kEuX1eq3B4rJ7hoMnulwSSi548xyNphsPzc0+12+7w+rtzQH33+/a0u32uHjFhfUc\njA0npKD48s0bXL3lfvh6Dvul7VEnZiTFomu1+t2tN1NfWaFChQoVKlSoUOFfw/vqmWq3WmMvkrgy\nARqNug9AbzTqvg6WsTnWittVayJxi29t7bAs2TV6n/b1+CD0QcrDYcZIRMhWVg4xknppeVLw059x\n9Nl3/vQZnnnWZb1FccDCnLP2T586wZFlF5i3MDfDaOJZ74TBbkQhbXzwA4/y+//idwFYvX2DtniO\nfuVXvsDTTzsvxeqNa7z8oxcAeP7ZH3D+vAsoz7LEC1wGgeLq5WvyCwG6PN1OesyM5uL5NwHY7G7z\nU592ehon7zlNe8b9brvZoV4vM+JCWhJAODvbJp8oLfKusPisFUvoT/hOKUpoDjUWydRajb0UoUVP\neLJKt0pmC2xRfj7O9rKFIZdyEPUoplZ3z6sD7U9+SuGDDfM0954gbIAxpS8l2kNf3QnXt1K2B5Pi\nkBMlQLw3LfOZRfvnYjpy8n7l0g5fft4F6m7sZhydd5/fvPAWRz9alv6xMHJeUJsNCNqO2lPBAta4\nz1WRk0rdyDRJGMkp/OrNHoOkPK1aT71FkeXJ09NnnkyWaZmsl3c3J7F3w7+p+7wdd+NhVOE4K9Aq\njfEaRQHhhAaZ9QSl9pp1Vhkv8mkLi5FabgcOHfWJKlevn/fB68Nh4hNndBhSyDjUYew1vIwxUP5u\nEHrRTj35nBYKO92JP8shkDn3+quvEgmdcfXaJb7y5S8B8OTTn+Sx+48DcHBhhmurLhP4gYcfYyC6\nYd977nv+N3cHI/70T77pfiAIuXrdrX2buzvsX3KUyo3uNit110/bq2dpRG5tvXX1OitLzvO1/8Ac\nornIHz/zHFrWZasDFNPTfIEeh3oEYeS935PZnzrAi/gqzcT1AbkkbpgiJYrLZATLjpSaqtWjca3R\nIiOTmo21uvZeWqWUHxdFkXutK62CMR2dZRN0tNoTKH8nGGM9pW/DJlHTeZRqqiD2vwX1vKz5maMk\n2agwhpGUDlNW01p2/ZwMerQlq3G4uc6m1N0LQ80gcevKqNPmwppbqy5b6EhIQvfMGdq5+3shrrMr\n2ek1DA3Zm3sodhL3zP/OVG3cu8a8c0a4E9J212giEdSu11qYBeddXdEhh465jMWZmVnOv+aSzNqt\nmDhyz7mz0/fhGAf2z4OUoftAZ87XIryepGxL2aHjj93H8XtdiI9Otc+unRx70+B9NaaCIPSZWr1e\n36eVzsy0/UMPhymBZEgE2lILyrprAQOhOm6u7dKQXeTAgQWaHbfJ5pklT93b2N0ducwY4M3r6yQS\nbzXfbrGwzy2G/+7f/Q9JRQn3zNkXuHDtkvtub512U6gpBSvzM1O30WQNEllUP/szP8/zz/8QgCvX\nLzBcdTEN3/72d/ijL38FgBs3rjKS+CanCTBhhEiWoqND5VUp7RWnRe7U//2Nb/wpAE9//Me477Rk\nI84uUC8VgaOGH0zzCx2aLdfGLBsxbfqJwq8raKVRQuG5jaQ07iayNG0OEpOgbYaWTMSQupMUAFRh\nfSyds47K71pvZDspBPcfoijyi5ir8SfvvN/3CsmKgEDizJSO725zN2N19kDhMyCtMX5DVqogkI30\nwSNtzt50C9Qfv3iL9Z1cflcxkufpXr1BvisGcWuOQoRjKVIsZeZXE6NlYSyKccFkrCtqDNxzeJ4n\nT0u6+tkupZ5Hq645ttKauonh20Q7J0UyJ2N7/qL4f8OYstbe1X2NHceUKaV8sdrJzDtrC4wcJApl\nvUFkTOENjDwriMVo6czP+5ii+cUl8guSSTocUJQCofm4mJ9WCuXT57UfS1opfxCZpFwVTG3497td\nv7F3t7rMzDq6p9Gqc/ENdyi7ePktnv6Qy/I7vLTAjnUb1Fa/4OaqG4/1OOKg1C7dWhv5eXnq1Gk+\ncJ8LlUiSbX708lXpp03OvuXCES6//jqXbjjD5MBsQH/onv3k8QO8ftbRfLthRm/XZb7e3sj4wL3T\nFVUHiOJJYVpD4N+bpj94BwMqCDz9XpiiDI0jN8U4M0spb1hpDVk2aUwN5Z6BP7AFofJ/G1P42M3J\neTJJRweB9jUYp4FNdzFi4KAj1IwzpozNSaQBsS2oy+GzkyfYbalXmRaYgx353YiGhL/0i4zdgTu8\nRYuzDHbdu7BFzq44H5aPHSOW+KNRmmJ6juodjXpkXbcnrd8+x6DnDnhxFBHInBiagLRf1qy9MyYN\nKFcPc3x42HONj4kcx+AGGsKyz9GEHTfOT9z7AJu3naG0ieXIEbe39IcDXnzhNQBacYMVodN7VrEp\nNKVdanPsmKO5H/3YUzz48KMA7F/YT1zWVZxY+6dBRfNVqFChQoUKFSq8B7yvnqkkSb01H8djb8Fg\nMK4Gr7XyJwirIBfNkCTpYcqTn7G+5Mxub4iWkg5JWjAcyPW58dXMz1+5ykzHeZeUVhSiWXHw6DH+\n6q//2wD83j+3rIordGtz07t+daDQwfQ2Z7838to/9Vabv/8P/gEAqzeusCVZhGtrXZ+1t//AQbbl\nOUf9AakIiRmTe+s9iiPKshhKBz7LpBbHNKW8xuLCIsePuxPo0WP3EkvtwqhWIywDzeM6s52O9DMY\n6dsgCLHT0nxq7z/KfrJ2HEjotL5K71nhy8AUNsJI0KJRAVpOWhY1ofsz/gGL8uKccb1Bno9/qxw7\nURh5uqq/u4sug+DDOqEqy96MtaimgbXjE0moLEo8a7kd05TahrTlBJMazYtvuHd4s5v6IPggUCy2\nXMBj3IqxZX/bjEKyMI21ROWpy95GSXD/cHMdxFsURoH30D1+apYDc65PBsM3+ZHoYSmr2dlDTb47\ntNak0m9KKZ9Bq5TyXqo0Tf1p+269Qn8ZkOWFH9daRT6C3mQFhYxPZUOsZH0WVntNGkzh255lmadV\nkmzExo478Vs0QezWFau3yimKsgG2KKnnxNNRCjCZeDuCECSjs8gKn4Bh7duEjd8FYa3uM3KH+YhU\n6srNtGc4da8LqD247wDbW46e2z16hJX97vov/sGXaImHbXZ2wWcvJ7t9HnnYafKFYYONNeeV+Jt/\n+2f45b/m7r+o+px/xukZnV/d4eUr7v4zjQ4PHnKerzMvn2V5xa01C3NzfOU7IhSpQgKmXGsApexY\n4NFAlpcaUoFnNkajxIcVNBoRVvoyyQr6A+fxCYPIC0NPevRHo4Tce6YsdaEvsyz3dSln4pbXmdIa\n75kKdOhrmqo9C6O9K9HO0e4Nz0JoHZDLemNN5pMmch1SlGu6iWjL+qEHQ/RN5/XTWLpKsuKzxHtz\nFmqzWCmXYk3GQJ51vdWmI3vbzMIyoXj0guUOywddZl/SuwXt0jOPz26LLNC4m6zMvRjX6B1TaZPr\niynG2l5aKS+ErLB+jVxcOsBx0Y/Mz2Tsihjp6fsOsb7l+ufC1U2UhOxE8zHNjuvDDzz6KB98wmlA\nHj11Dy3J+gyCYBwcf5fr3ftqTIVhOBY2s9ZntgyHQ79wtdst72ovTMH2dulKtLRabqIuL876gTIc\nJV70srCKVDbczV6Pdfl8d5QwM+/ihkZZRiCLmDKKQ/uPAPCrf+ULZKLEevjgUeKacw1mecGauEWP\nHFy5Yxv3H1ihKRtoFGtOBs5N/oR9HGWlyGKuSMRoSrMhaTaQ9ubkkp1jjPEGgHvB41yqcqJGUeSz\nH4JQe+oiS92iBc5gqkuBz3ZrpmQ6yPLcK2Bba0nFwLwTtA4JlAid2mJPNp/1aaTjDcHYcY0rCNCm\njFUZiyUq7dz25bOUtJ2x1mda2Qnl9SK33pVv7Tj+zhXRlRg7pb0RbCeyRKaBE5mUcaqDcZyXUr4u\n4lw74oGDbgLe2BhwdS3xz1OKy2ILTh50m+2TP/sTnh7KehtkWy52RcUh2dBRJkFjBdt3GZxRMiBj\nTGWWRT/7oxwji/zJAy1E2YO1bp8fvulc85+boo1aay+KN6kyPmk0NRoN//dkkeG3C2f+ZTWyiqIg\nl2cOsF6BPgo02pbyG8a/a5hY5K31RnGe59RlI7505TIXL7n06iNHjrMrWXOFMZ6qNtZ6+s0a47MC\nyz4GaKqAoBwmE33uKJDp5B/CWoOR0MX1MKQoMwgpOHrc1eYr0h7PP+/iSjrLh9jecVTdKz/8Hk9/\n5DEAWo0GQ2nHzMIKtbqsj1gas47+++4z3+UXPuMywp77zkVefNMZlFs7I7RxfTPIFW9suDY+ed8K\nNzackfWHf3SOXPrvkdP76O/uTtU+cGrW5UHCFGPaPwgDahL/mecFIxGcTFNDJJR4ZnIS+bzVqjEc\nFnLNCCSeyBiF9Wr1in7fHXK1Vv4wa8z4MJZnBYUcikJtxsLDYTiOYbXmz5UeeUeowBvTxhiMFNrN\ndUioxrRXYSVTT2l2JJtZ0WBWpG903mNdLu/ZjKasqXpU0BLjMcLSlGz52mgIUr826d4mFMOt1Wpi\nrl6S745Qsy4OLsmhIVMlVZqN1vQxmtaOJSXeToGOD+TjzwwTc2KspDAZBUKjOcM997u6ubOdWa68\n6ai926tX+eTTri33rG4xv8/t20dPn2J+0WXi3nPPgyyK+G4tigm87suYrrvbZa2i+SpUqFChQoUK\nFd4D3lfPVKPR8KdaVzZGgosn9DoctSClZYqCLfEKtZtt7/mo1+OSAWFrt8uNW+6UpOM6/ZE7Yd3c\nWEeJ7sfM3LwX3euZAYG4J2MdEYfO2v/gw08wL4HmURT4ZytQrG26E9Y0nql9+xd80KNVxrsqldFo\nI4HGoSYInFu9qdqgyoh7JlyeenwKC0JK/sEaOxF8bSasd0sRCIVa12g5QbSaIWFY0jbF+J7atc1B\nE0XTCT72d4e8dd55T+yE3JD7dxlQi6c6J6F0D6W60tTQu8vtRO28yXvOzc5y9boL2n/51dfHXjil\nfA3GMBzXPtvudgm8R8DrSmIsXq9sGlg7PpWEAV7XaTYOWJaaWCvzNZ56xGWUfvGb16gJ5VALFZmM\ntVY95BOPutNPpCG/ecn3TyGeqVCBmXX0bNzZx3DNeREGN69i8rLUytjrcfnGDq9fdd7aW1tDHxi7\ntVvwZ2fXpm7jYDDwVGYQBJ7mc783DkYv50EQBN6TZYzZUy+vHI93Q228HzATwqFFUTBKxBsRBFhT\nem7tHm95uT5FE54GFQaEcprvDfr0uq7/47jO7du3/f3L7D+D6BFReprG/eLXvywl9GKUhb8mrk2f\nLKGUYtR3Xp5GpKHl5nB/kFDbcd7Odlzj0nU3X//gD//QB503m00arY78ZpNGc0b6psWpe9za9Mj9\nyxw77jzrM9E1/tnvutp/M8EmRhJPrm8lLC5J9lkYMt9x/TQzO8+X/tjp+Q2VZr7pnu3BUw/wzIvP\nT9U+cElF470h99qEURzSbJViqyFpKv2a5JRx5kZZEM2mNLMYn4Cg/buNwwZFXoYbGC/4aQ1kRRlK\nkowD33FzFqCYEAN2IsHlOx8nsEwDU2hPwzmKcJykUIoQKiAXr5POLaE8WxbV2Imce/pWnpAI+5Ea\nRSHPXIu0D6kIRinzZQflGS0JE5mzEYO+m9O7ScDtnug70iRpiMAp0C+/GmjyKfcMcGNVT9B5Zb+5\nviqD+8cePeeNmtAwLDMBrfXZ4cZYmrFjB46eeJDlFRdQvrZ6ldUrrnzRqXv7HJByXfuOnaAz6+jL\ncCIpSetxn8NYnJh32MPeDe+vAvrbFt4xXTAukDoaJQwknmhnOOLWtjOUZoYJReJedlbk3Oo6/v72\nxjZbW25xK6wilwkThJr9K1K8V2kS4c6DQBFLRH9rrkGjIyrEhOxIBsPC0gzdHfd3fzhiZ2cwdRvb\njWg8IKxBS/0tV/C37G6FKUoqK8fYsVFRLqpKab+IKKU8U+aohzITZXwNypKV9CWaGcngC7QlFSVc\nFWoCGSCFMkQlz+CiOaZqX7e7zZbUuJoOe2MJpkFdNvann36aXIzjNE3Z2HCGlSkKHwO3unqdo0dd\ndpAOY++Ot4XxEyRLkrvKUCuspSFu9LlWRFNio44u1zi07AyKutI89vGnAfjKD76KvjUWeisX9jgK\nmRG6uHvmNfo9d01rdp7ehotFMabg0Akn1WFtSu+qq5E4WO8yFIpia5Dy6gV3/YsXurx22Y3NQVrQ\nlWLIqYWLN6anT5qNhqdHkyTxFF4Yhj5+MY5jb1hN1s5TSvlroijaI6p5N7Xz3o7xAvtvRvBzNBr5\nuLnM5KQSP5UqqMXj5x/Ha+41Bst4PWMtmXx3lIz8KB4OhgxlfKogKLVs9/ADWZbtMdYm1dA9tWoM\nxcRGEwTTtV+jGKYigBlaNted0GJYq4N1sVGrN6+yKUKU2/2hL3oeN2rEEic1MpDKYWNuTvPYI06q\no7vR5aXnnCH20IkWSx03D869scNlyeA7dOgksy03FzsdzeF5d/8v/asfUKtLlpk1vqz59198g25v\n+rlorPVxUnluKEM7DYaoJofufJx5ScA4vkYpf7hyoQdCdUURgWSJmwJPsemJUApjLIHUSExGBcYI\nlRZMqJtP1JJDWS+CrBRetHiqNhYjTxG5kA5V3pKy6vtkNUNrLGmZUW1SjMT8JTogKGVcQosN3fvN\nwojdct2PIq9Evr0zIOrLb9VjJNGeUZpT2PF4LGNGQTEKyjlhOHbk4NRtLO8Fe0M54J1jMfcIfOpx\nTO3kFjJZGtagaMw4OvLY6XlWjjhKetjv0Zxxe2HYqBOpUr5H+ThkrfUeWQv+gmvYX66jZIUKFSpU\nqFChwv/HoP5NnQIrVKhQoUKFChX+/4jKM1WhQoUKFSpUqPAeUBlTFSpUqFChQoUK7wGVMVWhQoUK\nFSpUqPAeUBlTFSpUqFChQoUK7wGVMVWhQoUKFSpUqPAeUBlTFSpUqFChQoUK7wGVMVWhQoUKFSpU\nqPAeUBlTFSpUqFChQoUK7wGVMVWhQoUKFSpUqPAeUBlTFSpUqFChQoUK7wGVMVWhQoUKFSpUqPAe\nUBlTFSpUqFChQoUK7wGVMVWhQoUKFSpUqPAeUBlTFSpUqFChQoUK7wGVMVWhQoUKFSpUqPAeEL6f\nP/bqzZ41VgFglUYp9zfKYovcfW4LClMAUBQWY529l2aGwcj4z4MgkOstxrjvGmOw1gKQ5zmFSQHI\nspThsA9Ako6QS7DWYG0hzwA73TUARttd5pcPA6CjELmcv//LP6vu1MZsMLRZ7p7Ht+9tz2ZMgTXu\nvxkUhbSxsBoj1ytVoIy7PrAQaNfePIBkOHDPOdwlS0cAPPfCK3zjW992n4+GnDp5CoCPPPkxjhw5\nCsCt26tcvXYJgMuXL5Cmrn8++pGP8dBDHwTg/lOH3rWNT/7YPpsk7jeDmma+03HPWwRkO67dUSeg\ns9By/aEyktD1vRrWGA7dd2vNBvUoAqA/GLG9NnTfJaC5EAPQWohBuWuW5pZIR+67w0GfztwSAOkw\no7u5BcBs2CaKa+4+7RpX37wBwM1btyjk3PDaC7fu+A4f/8hDVsvMcO/QfWW3N6C/6/o+DAMC7T6P\nopAocu8nCkOQEWOx5Knrk6IoCGXMrizMsztwfbIzHJHJNXleEIbB+P5yfVEUhOF4quZ5If+f+zFl\nrUXLQ599/fId2/g7X/5XNpDfUlgC5frHGkUYur+zPKMcy2EQlt2AsRbrZwVo+W6gNda6EWyM8f1g\njAHlJ53/nrUWa2ROmxwt31VK+7ZoZdHa3T83BuS3vvDzn7tjG+/71CO27J84inxbiqIA+V2Tjd+L\nUopAGqmAoaxJUdhgvjkPwP7lfRzct98//0bXjb3L1y4zSN07taHBpu7+2uiyG0jShKJwnw/7Iwb9\nnrQ9QwcyluIYHbgxv/bW9Xdt49e+9T3rT8PK7llvyr+UUlj5l1vrXFuNNezs7gCwtXkbm7u1YN/+\n4zSaHX+9X6KN9WPNfV/eLQrD3ncqF+x5Vr/OTlzzuc9+6o7v8HOf/yWbZZn/d63m1oa8MIyGhXxW\nI665W213e2glY0dr966BKIqp1xsA1Ot1chkLrg0OhS3Ii0zun/l9Baz/3VotpFZ370ejKeQZtB7v\nZ3Eck45cf/7O7/z2Hdv4+uXrNrMy75UlkHmAhfIFjFehciq5f2mlMMpdb3VOuLXtPr9wlQJpy/JB\naocPAJAGlsiU40GRq8nHm3xn//pjK6zvrFwFKHmn9x9/9z0D4Pz5CzaS9WbfygoD2Y9v3bzJ8r59\nAHTas5jUPbMONIGseRbL5o5b+8MgoCHvend3RKtVB6BWjyly6QdrULI2K6XIMpnHceSbqLCkMkZ3\ndzNubiYAXNk0zLfcdz98qoORd1Fv1e/YxvfVmAqCkFCMgiTNSL0BZVCyyIRaEWo3cI2yjBJZfFJL\nVpSLni7XQvI8ZyQDN01TP3nyIseIMVWYHGPKyRygZHFWCpABYW3B9uY6ALcun6fZWQCgFi94w2ca\nFMbIRiIL2cRmNzamLFZeZGFlkwCSzDBI3DNbZWnKpDVpwu6uW3jXNjepx+61zc40yGWh2dxYo5AF\nMc9SXn3lJQCunH2FZtPdZ5j0CcSAiqOQ9aH77gs/eomjx44D8L/95j9+1/YlJieR99YkRuYxKg8I\naq5NkQ4glz4OrR+Q2IxGx73bejsiwj1XLxkQygBu5nXiyN1HWQjlmmSUYcSIyNKUG28H6Z1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JBsQh3a0EFUtpDwJW3sUknkczb51Au0NtortQ9gd9zaod5TS47BmaKZgIXPaqX3HuzA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\nJfNkSTqwGovp6hDRh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Cg4PRb5IawXcXs9CH53tqrQbtPfnRiBp1wb8engGDs7lHXyBdCu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vshkEJzV0q8GpN4NXFjpyk/mjx4+hqRwrpMCNW64EG4gYT4jFtj7QqCZUTk5zBNItOoP1NZQk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SakaSWU5HFX9zCaUmhvmmTocEmxWmsFin/LFAUMa5N5yoPPYe28yHDSo+/eunUT2wOau6eX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dre90dgjl61I+GlIJOI5daYUaXd4nAlh0OK4GQiHnfUhpjYoNwyCI0K9V6sMInpIOQp+p\nV2YZivpyaAzmdd1Ra1HwoZPO55hzLcIizzDhrNlrN95HxmKkUko4PrEeRyB4lmeLvUkqzJh+j1oa\nU94XfvL+JxhzmvvKyjp6Ce+JkGhx5QAtAy9Kejic+biktSSCMBSbs7m5gg9u3uX2GvTZ4O62uwsl\n8v191Kfw9VsPcOvBoW9bfTkJlENVLH8IB2Hoa/yZymA059AHLfCFX6B4xAsba/CBrafSsKuqQpfr\n7n399dcWCvWVw3scO9Pr9nDlPK2trCy8oREUDiU/TzjnY5G6rQ56HMsYWIvLXNWil7yKCSvE3919\ngPXBYOk+AgKaQzfCpIOcQwOkUuj36BmtrQ98vTzjrI93q41wgOLz6ou8dRbzjMMxSouMs3Kdk5zR\nB1RCw7L5ItRCqV9ICeulhOBlaASsp7KrsoR4jLq1hydjPOCs6HYS4SzLJNx7sOcvigKL34pbbThe\nZ8YB7VZd+LwHeZe+Z2XQQ8JyMMfpGIGqizBXOLx/EwDRxC8+S1Tg5fNbOLvJItqtBAGPuVYRzWMA\nVy5dhOLY3yRJEKrl12ND8zVo0KBBgwYNGjwBPlPPlLEO/vIshde1yPICK+xyi1SMnG8B07LwJUTg\nFjpQUgh/gxMu8dZ1VkhEfFUPVADNtdAMpPdY5EXpLWEppA8stbDeEpZSempKSOGzZJZBneUCcHX6\nKevrGOtFAIUQAAtstsIEE85c+qPJCDnrM/1Cu4NnuHF/S4b4qiTL+X6kkXY4GNLmGHGNv7SqIPkG\nFzmFqSSL/abO8C7r/dyIApSsz9W2wo8nhIBdso+h1OCkCSilfeV2ay3qsNtcLmL2pVQQPtvOoeJb\nly3NKeFN7bWTpBQI+XZ4/uwGwpDfE0rE7NZf39hEwHRYURqEJdOeKoQpafxm0xMY/v7CahTV8oKW\nSsiHEjgj1ul5+pmn0T8iV35nr40TpmnSNEWa1l4JLLJfXIgjDv49zCp0W5yl2kowY82mLAwQMJ1L\nnhGmK5xFzL/b7XawzaUVttZ66Hd5rUixaKcM8Dhpp1UlEDA9IJPYU1qRtNBq8bzavC7zcoyCgzRl\nkECz12a9vYaSn10aVHAs6FYWApJrSKowQZ6TF89mC80e21HIeV2KAKjqQHMdQnIA73wyQy+k8Wm1\nDMpyec+UdRXY8YVz21s4s04etJV27Nd0VpQo+AZvqgornNhQVRUy1vKBcwvdKFMh4+zU+XyOKZdh\nyfIcc177FqRJRm1YaEsd7T/Ag3vkaZ1MDuBQ67JVfr+JogjlkgHoFy9u4uCQPF17ByPkc5rvYyFh\neF8osgLgTGBjBXosZCtKA8VhEMY4gL3HhQGmPGc3Bys4v0PaaPvzDCHTtk9dOI84ZD2/w2NMOUNR\nCAtRC1c6gbJcCCXX5W2CIIG1jyFoKSVsXRex1UbEnpGXn33aUzNKSD/GzjkfwH86EcgK4cNHKmcR\n88R48bln0ednPh5NHqrDWnuynLE+21EphZwp4mKeo+KQh24c10OIJE782loGSkrvmQqSFsK6xp8U\nnrKfTFPUJJiSehGecqpfJGBM36lVgKqsa0UusvachX8WAuJUSA3VD6XPSp8oBBgo7ryzFgHv92EY\nIFySzQCAssjQa3FZmqSN+2s0f+7evI6qLsVlgQ7vKxcuXUaftcP2Do8wZAq1F0cw7EVP4rbXosqz\nOQTXo+0lAc6s0vef297GDpfV2Vjpoc2erCgMKLmM+6K4bBY9/9pzaintdUl8ttl8gH941lrvop4V\nOQK2QbrtGEmtPGuBjLnS0lY+JkFDQPOilZB+ATgjMOYMFU/60o/6P0/zykJI76oUQvoNU5yaoAJi\n6VpZwMNu17IsMWWqyVnnue0oihCwMdg1Cprd7WMl8XszmjQ/nmS4Ilk8TCo8y59dFxJb7OLVUnlj\nMHUl7nPu+gcV8Db3/30JjHqckSBDtEwt27AQL3XOLb2BK7E4wIVzUPz70gEVj6tVnJVJP+TT6+EA\np+qFqRd1EQV8AebT9kAribCyQvE1eRz5gsZSa/9I87zERh03FIQ4OSFqoaoWoojzvESaB8TL1wAA\nIABJREFULZ+q/FeZJEoLrK9Re5IkwsqQFuxkMsGMM3DG09ynv+MUXVyVFepFanQEU8fz9XpQdVFa\nOKioNrjhN6tur4c13vB7iUZYU6LOnRIHfDwn88rqps9enRQp2j3auCIjYLgCgRYljOH564Qfz1Aa\n8N4PrVsYzclQklWJGRuVpbWYczZlf72HshajDB0Ex9uIMETFCvdVOoZzND7t1hpaPCZW54h7bASJ\nDk7Gy/dTAF49PQo1klrRXAC1RsdofIIbHIMxHE2wzgZXnEQQ3Ob+YMVfhPb29jDiTCqhJCYjMjCD\nIETExn6ctCBZcbrI5sh4fPZ37+DBfc4GRYFA1sWTQ19vzDnnqZRHIZ/lsJxirl2IPqeDO2X9BaY0\nJZzP2AJizsLT0anLbJFBcgjG2sYaBF/0buwe4iJnjroK0D4LE8hm1O8snSKv92Ud+MtVokPkfMnp\nDVaQ8frL0hJSLJ/pBiURs/EdRpFvcxwnlKUNStSu92hr7UMGUX1mRFHsjYW8LHH2HPUrDiN/PpgK\nnibrdDr+b4nF6hIOiHmNVsb4LN4gDNFn+ZU0nS+dkQkAKtALyilKELIzwZoKjuMZnHUIOQsPp/po\nT8UYO+u8JEcYhhSjSj1DHXlCdQ65dqzWEKamBeGzAqWElyeSUvqLrnOLzEEB+1jyDyEqbDBlOZpl\nOL9Cz7R87jKGTE+P0xwqoNdnaYbScM3SQPr46gdGouTLTFlUmM3ocjU6PsHGCn3/hcsXsLPOxZzP\nbKHfoz07jBMEHGqhlYCqjVZYijkFnff1/AfUQ7bDo9DQfA0aNGjQoEGDBk+Azzab75TbzFl417Z1\nBpOsDla1dFsAWcXxqYDMWq+F6kXVbmPpTUIH4at1O2Nwis/zLu1AA1Vd2845+PJCDpC1sAgW4qIQ\n7rEC7chyX7jsK86Ayua5dzsHQQAX1N4loGe4fljcQ8Y3x/15hk/mdPv7jq2wUXBNQ2Oxxn0ZhAlC\nNqKnVYY9vgneNAa3uaxK2e2hLfhmV5QoODMm1RaBOeXSXrKPzjlfNkOAhE/p9cV3OHtq/CTgTou7\n1QH/7vTt250q0eC8xlcUxwvRwCDw4zfP5r4OnQC8B0TahWtfSuED0MfSYRwsV+/s0zsPHyjf7bYQ\nsoZYp9P1behOU4zHk8UHTkHUdRe1gmDqJYwTOFG77wU0Z7FFUeRF6zqdNmIOClYPuc0es87RKVTO\nwjry/sS6xHRC2Y5BewuBqEuLGMw4K6awYpEFZCVmM/K2dJMY4Nu/MgL5kNexVOiH/OzSGWS9jqXy\nFFSJEDHrIRWlhLQ0nkU2R53N1467mI45w64VQwd1kPqjIaAh+XvS+czXIWvJGHO+De8eHeGI640N\nqxYO9ug9Sk8hmLLsD2fosKfHZJnXRBNaIubSQVpptFp0Gw7ixAcvl2mKvVtcU+3gJmJR6zYF/vEV\nRe69UdYYL+D5KNy4uYuV9VrINkCc1J7SoV9PRTqH5nnqjMOcS5XEKvBedHHKM6yDACGLN54c5bj2\ngMQ2N1dXwIwshif7iNhL0h8MUHCJmjSdAmBqN0oQcwKNVArW1Fpkpd/fl0FlF0HP88nMZ1KWVek9\nq1VlvNcfAEL2HBXlYs8Io9DvT6pQ3utU5Pki21hIqGDBTtTeLi0VUvZGGme99y0rS1T8nbEOUNVe\nfylQnGIoHoVWq4UVpiw3JyVyDteQcNDsJQmUw7wVc38rT/NBLLxpzjnvyYriyNfztLaEqeeXXSRd\nSaV9+/PSQnHpIBVoiJpBMAZKLZgcqagNZ7fWMegvX04GrkKHM3GrQmJ7vc//YfHTDygZYDo8hmHx\n4NFkjJCfhVbSB9yHMvFhFCfHR5hN2DMclri4dQkA8PTli9juUztXel2vsSWCyJdHEgKogzkEBBTv\nT0opGO+RFHDuoQ33U/GZGlN5ZXzEvZTKiww6KX1h3mlWeU5UKuVF8bQDhK0zwSpfQ6ssbb2XI1Dm\nVKzTqQ3JwRtWUqmFO845z4VTEa3F4VRnoAkpfSbgclhQO2EYwsR1yrPxizPQwWLDdA6cPIC+C9Dm\nhzfXDoiI2slnFXa5MNZ9ZfyBqrMCAdskpdAoa9dsACScRdZ1IaSn9gzyOvlXaIR1JtWpjLhHwTr4\n+opCqYdSw+vhM8bCoP5N6Y0jAIssOSH8Bx6KU7DWc9laa29QDEuLAWfIDPqDxXc6QTEfoGK2iuN9\nZrPU16Oy1nkxzyfF6aWVcHp1GAbodOgA6vQ6GKzQgW+MWWQvYjFucIDkw1kHof87UBIRKwwnSeyV\nlqUUP+fK57Y8jmrez6EoK1gWTxSi8gKFeZ5D8CGYFxUMV+a1CgiY5pmMhrCssB+utTHh7L8iTyH5\n8BIqQDuqxyFFxSexNZXPSoJwCDiFPC8lTB1vYwtP44tQQTFVNx3NfJzdMtBKwbIBePPuLizTr9sr\nfQxnizp2hjMTK534sS1MCccxJ/N0jkjSOHQTjSSpN16gLibmhMCUjbVqMsJ0SEbo0e5djI5IbNhZ\n62kSY8zDcT6cun5aouVR2DibQIf0HLZWBhgdU4iAnY+xxdlSqsg9rZZEERTHVmSz1AuFBkGIhNXh\nrQMy7reKO5ixlMMkcwhbLAJZCtQrYV6UPutUSYWYM32Ntd54EbBod3ivSbRfN8sgG8+8DENZVT48\noqpKvyaiKMKMLzPO5YiY9s/Kygv36izz421PKTULtcg0bHfanhbc29v1VFcURbB+HXvZSmRlCcEX\nofFs5oWBpZQLqn8JrA56ePYyZRSu9HowVX2YO3gVS2e8EPLpMJTTtKax1u9PSikfV2xd5Q13ovNq\nuROKY+YvXRRrF/AZmvKUKKi1zgsJr/V72N5cPmNxbgzGXP/u/oNj3GIpiN3DYwxPuC7edISMq3hI\npaBqAx8LGlcFsd8DbFX6wvNrvRaevrQDANheX0ebD8YgjODqM8c5L1RrnUCJBZXpKV0h4OpYT1d5\nangZNDRfgwYNGjRo0KDBE0C4x6CwGjRo0KBBgwYNGjyMxjPVoEGDBg0aNGjwBGiMqQYNGjRo0KBB\ngydAY0w1aNCgQYMGDRo8ARpjqkGDBg0aNGjQ4AnQGFMNGjRo0KBBgwZPgMaYatCgQYMGDRo0eAI0\nxlSDBg0aNGjQoMEToDGmGjRo0KBBgwYNngCNMdWgQYMGDRo0aPAEaIypBg0aNGjQoEGDJ0BjTDVo\n0KBBgwYNGjwBGmOqQYMGDRo0aNDgCdAYUw0aNGjQoEGDBk+Axphq0KBBgwYNGjR4AjTGVIMGDRo0\naNCgwRNAf5Y/9p0//JGzEACA8XSIVrcLAAi0RKjpdWcNoigBACitkCQtAEC73cbe/i4AYNDrY5oW\nAICDw2Nsr/cBAEmkUFoDALDWwTkLAJinBbQO6XVToaros6urA9y5cwsA8ON3/gL3+TtH94/w8uUL\nAIB//e9+C1EcAQDOXrwoHtXH//W3/8C14zYAQMfb2Dv+CADw/se/j9XeBrezhcmEXp+c3ECe5wCA\nSztfxGSkAAC5mKAbn6f+di9i/3AfAPCzD/4l/sa3/l0AwIVLX8HJ4X0AQCsEysIBAKrKQtHXoCgq\ndLprNM5xByqgsXUixHCyBwD4wQ//J9y5820AwD/5Hz7+1D7+w//jP3XSkQ1eyilSeQQASO0IB/sj\nam9nC1LMAABxXKHf7dF75iNMpyV9VpRAMKVxUhrC0nfGQR9CdGgMzBi5SemHXYDAUqcqY1CW9Jx1\nFAFS8usZrMsAAKEWsPRTyGcC6/1z9Hz+/T985DP86ivPu7u37wAAOnGCTpueZxzFUIqWjFQahsfh\nqavP4I03fhEA8J0/+ufY26V5qvXPLS9Lz6fb7WJ7awsAcDIcYjgcUhelQsVvdc7BWpq/xlq4ivoL\nY2ENz3Fn4Rx9Z1GUkBHN0zffefeRffzmb37ZHR1NAABCjPFLX6P2JK0Y26tXAQDPP3sFk+oEAPDB\nrTtod2NqW1GgHa5QG+YJ2prm9VNbF/Dh7T8DANwd7aPF8+5o9wRZQQ/j6WfPohXT3xYV7t4/BgAM\n5Dm4gObMtRs38NrlX6f2aOBHd/85AEB1NJ6+/AUAwN//tf/2kX38D75y3t24TWO7uRViY5XaPzvJ\nMZvQPBFKIuD1LQMNW49nZgAeZyMk7k3pWYz0ANHKNrXfWlRVxX87WN57nHOw3IayLP0ziqIIrRbt\nZ9PhGHv7tP50K4BU1J0qyyAtfedkOP7UPga/2HZuTmPpKoewxftmL4IJ6aOmyGAL2l+EVoCgOavn\nBislzc/w6kWcPXeJ2jivMC7mAIDjdITZhOZIWqUoM3q9SlO4gnuYOQhL35O0OuiuDuh1rVCY3H9W\ngMbAOovKUP+qP/30/gHA+1Pnjm5/DAD4n//RP8TTr38NAPBv/Ef/CfKjAwDAP/7v/ytsPPMKAOBv\n/v3/EEJw3+FQVfS7cA6Q9LpUEgGPgwDguG0A4LhtTgBG0H5TGgctqb9aCph6jpSAYX+EdRahor+V\nkyj5+9/YCB7ZRwCuniN12x/xZhwc0NyJIo3ZjNbNmTM7EOJx/SM0Z/f270FJWgdra1v4q5vhuA0C\nOb8nBh7Z6H964pzk81hKv2VDSUDVz+WveF3xPwCQwvm/AwgofnQazrdZAtBu8dm6cRqLzyqx8CQp\nCP+3gIPiT5weSbHEg2k8Uw0aNGjQoEGDBk+Az9QzlWUZHBt4WmvvOSqKylvFJ0cjQNBtqNVKAMe3\ndgEMVujWM55MkWV064GzmEzJMhc2RM43RSGFt/LTdA4pyeukAg3D7zk6PsS1Tz4AALRnUyhDtuVJ\nUWJjhzwZQRBAPNrw9gjDyP9tbIq8egAAKMsDlBn93+G9j+FAr6fzkbeorSthXcW/a+FkQONjgCyj\nMfnCi1/F1trz1EfTRzth70V5DCFoTIzJUFX0pZ3OKuKYPD1CB5DsAYRyeP/9NwEAP3jr9xGFt5fq\nX1al6EbkaYqSHubTEf++w6BP3ookbPsxVkqgKOn2XNkcOqA+RTqEjKkto9nQ325hCsT1EDqBij0y\nzojFrUUBpr5MSou85LkgKoB9AvN5hUiRFyCMNCAWt89HIUkSuskCmM/nUPyAClNhXtI8CsIWhKKG\n9icpVrd3AABf/IWv4s3vfhcAMJ3NUHLf5/M5tjY2AQBvfO1r2N4m78Zbb72FG3d+QF23FZyg9ltr\nvWfq9N8OC4+Vs4s+VaZCLwyW7uOVyxkubFJ7tjdfwOYZ6tesnKDTpXGbpRZrWxcBAO39Oe7c+pD6\nLoAXn75ErydbGERnaXxmU5zskSfxwdEdKPY8dlsd7FykO2GcFChzaufwOIWqyGM8PDqBGtCzbukO\n3CGN24E8huUHr2wIuOXvf4EGwpB+d55aoF/fgB3qRUfeJfpdJYX3UQhbQjh6XagIrYS8WoWMUd91\nLQDFLmClAGsXbas9XFpr/7zCMESvR2tnPk+hExqHsioh6xu2XH6quryEy6iNydoA3R3yEM6rDHZG\nnjcVJHB8w7d5DjWl9z8vOliNyaM/nsbo/ow8HccnQzzQ9AzHkUTtiTDOwqbsjZo67xFQYYQooX25\nFbX8nj6bDZHNpzxOFcBrWgUxVLTYIx+FEkC8Qh7OM5cuIRiTh743uguhyFOGYubHNYwiVBW1IRCA\n4vXknKPBBVBVJU5OyOPqjEMck0dvMhnhrR++DQDo9vp45Ysv0W+trcDVXsqq8l5HqZX3qMNZaN67\njbBwYuE/+X8bxlh/timlIHkOLuPV+nlkzIpcv34DKTMzL77wBWxubvyl7689aEs4ox5CqAWkO+WB\n4uFRgv7Vr9eONSWEfz0AIHkeSoGFN8oJaH5dYeEZklh4shQEJC8oDeENHgnvqIQ8dcLLxzrtH8Zn\nakxBOP8w2u0WZjkthl63j1ZMG3hZOKRz2giCMIGtaFO9fecOgog2tChQsMb670lien04PkLJG2MY\nR36jq4yBY1poNh7izBk6RK59/B7GI1pUncMDWE2LqnAV2uu0OPOiRKe1/IKYpSkC3hm7LWA0uwYA\nUDKFK6lf2fg+EBAVlGY5NFNHB4d30Y7pkNWBxKUrTwMABt0tHOz/CACwvXkO3Q5tXrO8QhjSZ62I\nkaY0nuPJCTptek+71UMUEU1lpYNT9Wwtce/+TwEAo+MhzmzFS/XPiDlKSwegNNpTV9IBcZvHL81h\nKt7MVQWt68NHQzjaSEUFtJjOzTFDMWcXsNNwPJ1LY2D4IJJCwrAFZY2D4n4IaSEEG8paoCzr9wjI\ngMYmTtoAUzDLIAoCT0s4ITG3TEFnJVI24stpiVaH5sjZS+cxnY8BAFtntvDNX/8WAGA0GmM8JmNz\nMp5ga/MMAOCpK1cxG9P78yzDaDLi/pYQonajA/4gMws6zzr4MTHW+MuJtRZWLD9Pn7oSQ1d0SF2+\n8BSiNl1I7p/chpT07O7evwPVonmhXAfzGY1h1I0h2fi1mYWW9HcU9hDyGspzi9mYaJiynGHtLFH6\n87lDJ6aLislDjHbpAJoeDXFhsA4A6KkA92/fpM/25wiZxg9UiEOmxpaBVhZJRPOkyC1cVVMpC2vF\nWgvB46mwOJCEAhTv+CIIESvqV4QE81NG7OkDrP67qipPBSmlIGtjUCm/UQdaexojK0qomn5zgFqS\nMHCVQbRKY7P9/GWMMppH6TRFfZY7JYBjPkweGGxO6T/6kUOa73PDj/FgjZ7PYatEytRh6CLMcr4I\nFSlcRvNCWwHH7JXUIdY7NI9G6QzDE6L9nZnCsiGDSCEI6UKnkzbkYxw7x4f3UBb0u//WP/g3cWmF\n9rLZ5C5ibsO5p67C8D54dLKP4S7NuyhO0F9fBUB0q5mTkXhy9zZ+95/8n/Q9ZQWtmb6GxeGIDMCq\nBN76l/8CALDz1GVcuEhhH19643VvDGqnwccQjAMqXhNGCATh/3ekT8kXOoDmr65jOv4aZkBtOuzu\n7uL994lOffrqc/7/nXOn5vhpK9891u8FigwVgI2m2sCR8JdVJXCKqltQcqET/j3SuVP03MKAIaqO\nzyIsDDGFxe8+/H73l66yv64hRb/boEGDBg0aNGjQ4K+Nz9Qz5ZyD5NteGAY4ntLtvKwMZjPyqkzT\nDCFb/lJqhDE1cefceX8DVjpEi12zw/EYh0d0G9o5ewZpRlb7cDLEhfNEPyRJC1lKt5sSFe7cp+Di\nB3sPYDgAzwVAOqOb3YWtbZxbJcpqdHyMdEy3la0L5x/ZR60CxBG14cMP/xTvfUyUz1o/RJbxrc1l\naDNt0FvrYz6n301aChurdMu7ducn2EiJ4jRG4cHeTQBAu7OCz3+JfyxwmKfUNmMztDt8uzzeQ8SB\n+yoKIfnWJqWA4Nvw/b3b+Om7RC8VuYNWvUf2DQCEKuGYTizKAiEHWYcyQFZQW7LcQDi6BQZGwBry\nmOSlRcm33kgqxCW1JY4SCPYcilSjwzRiIkLMQM95OssQM//nZIlS0nxxukKL+wdnURZMe8QtBLr2\nZCYwVe2cfzTiMIJmz5eTCinfzktbeRoDUuGXX38dAPDq554D5kTD7h8fYzgkb2ev1/Ne0M3NNShB\n7bx57ROMTyjw+mD3LoyZ81cqCE8PwNNDEuSNAwDpFlRUYSoYJh2qyiCOlu/j5uYOItT07wnCNvV3\nTQ6wv0/PLqtKnIzJE5RlpaeL20mC/fv0enV0DH2OntczV5/F0RHN2aqM0VshD6aOCriKnsXhwQFm\nLeqvVusoJjQme+Mhdiry3OlM4t1bP6ExfKqHfp/WRBImuHPn5tJ9lMIiDJj2nTg/N6Rc0CTOUfBw\njTppwDnnqd4KAhkHXKewsFF9BzXeYyiFgKy9WkL4z56+2WutMc/qvku0eD5bW0Kx5zS0FqFczIFP\nQ7TaQ3uTvHmjcorpjNafwIISr4ZzRLfoeXaOJDYD+o+0yhDzz5iswP6Q9uLWHFjhvdXMKoxHnABi\nLcqKxiYNHOI+PdtV3UY5pfcM8hKr7LV+UEXI6GvgdADp6HVXVrBY3kv85u/81wiZTv/WV95AEl4G\nAJSlhJqQZ+3qWgt/8BYlPhze+hnigAPcqxyCGcV0lkONyPs6Hk0x2qd5WtgIeU5nQ2GGaHOyic0s\nbtwnWvvOO2/h2iXycFl9DVGvw2MiPLVrIOpHDiED6JD6++XX//bSfV0WZVk+xLqon090WRLOARGz\nPS+//DK2t8hjvLa25t/zadTh43hxTnumlFwEiyvIBd0mACVq2m5B4WksaGUpFr8rBHywuHbwbAJ5\noOrX3UN0njr196Nx2hP36Pd/psaUNRViNpTyPEcQ0N+j8WTh2hYSh8d0GMn1FUTc+0G/h7hNk3g0\nneHgkAyT2TyFYS72eDRDWtR0SIB5TotKC4vNTZog03yE6zfvURuKHJIPzeNYYzQko+brT1/FesAx\nEhrQ4fIc/0q/h/GQqL2/+NPfw8hQLFKsz6B0ZIRM8iHWOVPPIUOW0ma0cWkF0zEt+NIdYjihz3by\nBL0+Hcpb568Aig73TtKHcHSQjfKh585lEGOwQZy3iAUqyXFYIsJ8Rpvm2z98C7Zi+kG24HedRyDQ\nGpJn9mw2QcCGjIBGYTm+TRfo1plFTqMynKFYOsxy2vBlqw84zsIUXbSY6mzJFdh79PfJrsU8C7h/\nE4SXqY3J2RBjQ8ZI5eYYtGgMAisR8UHUbbegRIvbFkMHy2/gQRggDGgzrCD83DTG+szLpy5ext/4\n+tfp/WWJlA+jyXjsYzb6/YF/vzEGjg+v0WSILKPnvLt7HyXHfEmEOL2AT8dMGeYThFM++4gy/jiz\nz5iHjIJHod05j1DQPFLKonJ1bJFGxheSqO0wmdNamaUOORsCRRygsPSevbu7SEqilC+cv4LpjMaq\nN1hDa53W0Hh8ByVT3FpF3qjpaIksJ0omlccwBX326PYxpgGNpyuBVZzh8ZEIVLh0HwUcQo6Tga1Q\nFrQO4kR6YwNYxJ455/yBaE9tnlllMeIYpHlgcOEiG33KYDplWqiqfGyg1AECfhZlWXoaRgmBfM6X\nAFNgpUMHN6oc1Zza1g4CSLncttzd2ULGcTrpeAzlaurbwQ6pLfpmju4R9WVDBljpt3hwLHx2lQ7w\nFI9BkBtE3PVZbrCh6Bn2E4GM5/VJXuBZjqW7dG4T+3OayyvWoMUD+9v3D/CBzXgAC5iK4xpjybGN\ny6GfpOhoWvcPPv4E3Rntla0zT+PuvbsAgMO7H2O0T/NlPhzh6mUKlThzJkEn4otfF8A2jXdVtPFi\nQfvjeFyi5FCSMDoHxaEBVWUwHjGdpiN0+rQPhfd/hPCQ/taBguWzp3AGxtVxjYDWTLkvbUzVa1fg\nlLnw0DuMoXbu7d5CHNE6aHe6CDh7vDQGos5iU4vPulMG0cNUlPPzvNtO0LpIxlTSThaxgw+9W8Cx\nsSPc49FaoVrEAkrpfByTdKcMqNPGlBWe5tMC/qIiAZR8uT0ejXB+jS4TgVjEOilBxhXwsCFGX/E4\nJiDlei6LhuZr0KBBgwYNGjR4AnymnimlFQoOnlvpDjC3dEsriww5a6EMVlZwyJkWk/EYijOLnKtQ\nsju2LCuv79HtDVDxrepoNMHxlDMqihz7u6TBdHZzgIwpvLu3b+I2B7eenByj4Bvz/vAIKiHP161P\nPsH/+NafAwD+49/6LZ+BtgzGJ7cxm1D77966h5khb42wGme3yROjoxyVoZtUW/WhC6IUXWZRZNTm\nIMqxtU03ssAJXLhEt//BRoTrd94BAFy9+AYEm+DGZlCCbiutJMCgT7cVoTUEZwVqJ3G4R67xbkvj\nl3/xVwEAb7/9J+hEy90WkyTxwf9WGFi+DWsdQTu6xYax9u7jYl4BtT5U3vb6UEErggqpjZOTOVpz\noig7bhOjQxqbXrUGLeh7Ah2ivEPjms4VQqZDo06BkJtuK2DQIXd8EIQYMz2rlEGULEed0GcDKF1n\nyGgopmPakAi5X2+8/is4e+EKtf/4EHPWp1mV8DfdNE1xwl7W+7sPsM3Zc+V8juFRHSSr4ThrrKrK\nhzVv7CITaZG5J3wAunUWog5u1ngsjZkw3IZSNM46LDHnrLCiEggSWiuVzaAVzZ0smyAd0VxOQ4uN\nM3Qj7G/28M7blByxuX4B7YTmsu4JaL7luzzGaEQ06GC1jUTRMxrdnqKY0Xd+9aUvQcxo3O4e30Fv\nh7w/WbXw3Mah9M93GQgIBOzxVKJAwTfaOA4hFXv6Tt1WnV3QqbY0KPnvuRGoOJtLqRTF9BAA0F1f\nRWeLPcA6gq01hwDvIUinE4yPiRIV2QiJoTkZaQnJbVvf6IGlxpBECQqz3G24UhZzTtaRSgOcfGOG\nc4S3qK/bQ4k+J3r02hqBrjWYFGYT2nM7SYCYvSpCCSj2diVJUOeXYNCO8dJleuarkUIqaV/O0cKl\nNdo3z231sDGgPr3/J+/hozsP+Dk4PzYOCigWAdSPwt3pfSClNYTI4gvnqQ12coC33nkXAPCPv/0W\ngoT21vVejHf+nJJ7ti/s4MUvXPF9/GTvOgAgdVOfnQcV+AxaKRxqhTARCPTYk7WzuoJeh75fCIEp\nh1YcTYY4KskrZ8oC2tWe/gjRY2QsEv7yLLnT+lNTPsOuX/sZYj6Tzpy/jPOXngUATNM5QmZUYqVp\nQvNX1n4bh4WHyMH6MG9bFovs1U/13tR7jIP6K9r8lyGUQJ2PIITwWXvaOWjUCSAWitujpfDtENag\nYg+/DiOfH5lNJ5ArtN8EWntKsigrGJ644ekM58fMdnw4+P7R+EyNKa01ipIF6aYT7O/RDqKVwqBH\nh6l11otkxkmMFmeICcBnZGVZBsmTSZ6KPTg4nuLWPTIWBp0WuiG72osSn1wjkczd/XveRWqFw5D5\nfoQr0EwhvP3ue9g/pu/5V65dwzlOY99Zoo8P7ryNiI0E6UJwOBSynoNlKjpuaRweE4U3KrpYG5DL\nPB0foqr2uG1HGI5Y2PP4FmbTG9QXBUDSwr5y8WXcu38LAHD/xrt4+jKJLZbzGfb/4zLzAAAgAElE\nQVTu0fvTPEeW0cZazHO885Pv8xgeodOjcbt8YQNaTZfoHaXS5gWNmQqAmpEoMwtpaYPt9gIUvMmU\nxngXvxYJBtR06KDAdExUXXascHWVYiEG8xUM+YAo0jk+/OQTAMDuwQOsDXhebPQRnqMNvHUuhN7k\nWJiwhSTizKLJMWZMzcRJCqGXNzTiMETCm6GBhGXB17jVwxbHFWyeOYv9Y3q4AgHWd2jTjsbHKHMa\nn9s3r6FkgdjpdIKfsfDqam8A3reQFwaSSX2xyESHA2DruB5R/ye97uqUYbHYzirnILA8zXfm7FMY\npxQ7WJohcp8FO0DOdG0AA61pLq+vh55yH04OcDCmg7K7sokRT/I//P1/hquvPUV90dpfctJUYjIl\nYyrudpHt0Vzbf2+Mpy9SxupmsIk3r32PPrsmkRs6FB7c34WoxTCrbWyzvMQyUDrwqfFaOhQZX7S6\npw+6U+KoxpFsAgBROZiSD1Yt0WkxDeoyTPfpULbTXcRt2reSTh9tFqfNSodRxuEJgz5Gezz/yxR9\nRfMhhEGZU7/aYYQz5+hQKI3FcbqcsVHkuRdzDVSIko3O8E6OLU7U+8qFbayv03f/6NY+It77YgUU\nTFGaKkXEhu9qr+cpqjzPSF8CJBsxnlK7vvzSC/jJTTIof/bhJ/jil14FAFz60tcwuUExoh0BFJz5\nqnqhFyst7BS2XJ7ms4VAl8f+8pk1RANaf2XvHG4e0PoeTgrsdGh9hMIiLWiu3b2RIp3QHvPlz1/E\nq5s0N2fK4l5GA7Q7PkRtDARSYq1Nz/Dy1g6e2qAMvjUZLIQiW4mXO7l15w7eeUD7+I3yCEcVC5wW\nQ5/BuTwenZFXf6OW8FIdUggfh/zRJx+g3aINdmfnnJcFCYTzMUKnbQMBi5pMU2IhJUSxR/9Pg15A\nnDK0Hk9CIBTw1pQ8ZdwpWCjeJyKloLmXaTpDxPtuOwywyyE43fVNH4+2vbbqDavgVItGs9SHV/S3\nlt8vapw2YAs+O8P40Q6VhuZr0KBBgwYNGjR4AnymnqkoimEdUXu7D+4hm9NN5/Of+zwcU0dZVqDP\ngZlBqKHqW1JZwPBNbjSeYdCnG2GeZ9ib0u1jnpbY3qgF5ARWOGD9ZHKE3X2iVaqyQDqjGwScxTHT\nMGcuPYsp3+xam5voMb147+AQLb59LoMy3UU2o89qJSBZCLSYlKgy+luqECk3IbQF1teoj3G7i4qF\n7rpq3dMbhweHiDSN2+G9XQgW1fvuH38bt26SZyod3cfxXbolFRbkNgKQlaWnKExZ4t136PZ/dHQT\nX/qFFwAAG+tddNtLBvZKAUjWnnGVp6KicKE9FAYC6ZzGNTcpBOsfGVd4Da5q7hDx7f21nS/gSoe0\nTf7kd9/EvTtEdUZxDBj2IhYBXEm3Ro0uQtQUUoY2iwaKMIHhNlS5QsJZga12ibKaLdc/AKHWPoCz\nKo3XA4qSCIdDupH/zu/8Nn7zN/8eAOC5Zz+HnIObZdhBm72mg0Efx6y7s7q2iru36fmMpykEB3wX\nRlIqKSgzZXFzFKj1AI11iwwo5XzWCudo0ruFhHqMq1HhJp5OSuItFBG152gy8fO31R57L8XGRged\nDmdk3bM4OaRxiHodfOFrXwQAvPOn76Lktbix0caHBxS8fu/k2Afch3qM1hF957raRMzezN/79ndw\nb0LerqoVQGpaIO1YYcTaP+t9gzhcLusUAAKtULsAVSB9VmavWujdnS7bU1WVv4+b0no6W2mLQYc8\n5Je3trGxRlTj2c0NhPysR6MhLHshP755H9fv03o92m0BJevpBRJl/bxMgZAz64QrUE7Jy2JNidAt\nty3b0iBgr0SgFQoOOu+MBF6/Qh6cZy+s4oD3vs2ORMWlarI0RTus5yBQzyMtBPoR9alSDmMOrdhY\nGWCF99wffXATtw446LzTQp9L19z95EMc3OT9NI7wyho9qxNhsMfhHWWeem26ZTDPKwSgz/b7EUIW\n58yiABmHFbSD0AtmSit8Ka08n+P+HQpS/87REf7Or3weAHDl4g522IOjzz6DDrMicRyhwx7pRMdw\nKfVx7+P3sP+A9c1aEc5eII4inmc4k/I+lA1QbZJ3/V6xh6wcLd1HAItMwE9x91ScqJJNx0hH9P2r\n62dR8Tx9660/x4BLHY2mOTa2yCO5vd5HK6z3GAlxWniTf8/Z05TW6SD4BYxdpGXIU59dBqEAamVa\nEtWsg84lLP+9uz/Ghz8jRsUUu9Dske5ELc8U2DTH2TMUAuCKAipZCLT63woClGb5hKOHsRiHe3d3\n8b/9L/87AOC//Ef/xSM/+ZkaU6PRhNSoAWxtrmONB6vfiTHltNVWEEBy/Mnw5Bg9pvmgNE5Y6bxw\nAsdHPJlWut7Yuf/gNqBowrW6LXz/Ta4TtvvAUyPSzLAyoPcrIXHzOrnsMyhIzi68VxpMOWtk9+gY\nZ88vQ/ARQlmi4oXd6Wh0OfVbVAaBoI1ge/0cPvdVWthn1s8hZsHSbrf7f7P3JrGWZOeZ2HdOzHHn\n++aX+XKqnKqKVZVkDRxFkSKlVqslt9qmoAZswG4DbrcNeOGFAS9644WXNuC9ARtwqy2rAdluUdRA\nkRRHqYossgZmVlXO+eb73n13jjnihBf/H+e+gijlLRAoePH+RfHl5R3inDhxzj9934cir0g1x1Am\nvT9LLLjWlP/2MeOHPEscnO+Sw3h8VEOPdc7SLEXCpaZSCH3IhuEAjkkb01LHxfoy6bGtLnVx89qV\nxQYoJUyGhk9noU7R1v0aXIYw52oCMIIQRqr7U2QmkEQ0HzVnA1caVOJ5sXULwx0aX6fVxBZvVu/c\nfg8FOxpLq5excY02K6s9wdYn6GD3NkpMcjqsjvtDlOx8pamCyf0DzXoHY2ZOXsSElJqw0XFtgA/M\nk34fA043oyyx/ZhKkJvry7AY8Smg0DukDXw6HmHEtB2jwRiuzei28URTV5y7eBEFO2tJHCNlxFxZ\nqjmkt8z0ZgsI7dwpkEYWAEgpNVx6ERtN++j36eCzTA+uSTQiYZjpZ8txoVntn+z1cTKkHitV2Lhw\nju5RkQmsXKC+oVvmC3j/xz8DALy2/hzakuff8zE6pLWZWAriiA7oJ8dP8PCIHMzmpRWYgp7L+zuP\n4XusbfbpVxGMpvw9LTjO4s6UKaEPVssWmAU0iaerTKosdZ+aUgWKhPvUcglVlR9MibV16tXZunIe\nyyv03Ny4dgMWN6Ac7T+Bw4fFRreBzKQ1sH08hs+UJY4BVFKTcU5I0eo6obgkNvf/nmqWtGDVad2F\nQayvd6nl4doGOXz3eye6lWGt28HuHjnBskjg8RwXsYTD63dtqYNNJvAsAQym3BNUKAjuBYyzAi1G\n624stzE9ouAn6u2gYvw+16jjt89TmWxSZPiDXWpZiLICPve5LGLKTBCw0z8IRlBM0KtUCcXHl+VZ\nKBk5mOdK9w7mp/QSB8MAf/RN6u371LMDvHqV1u+lC+tYNn2eT4mUKXqG0QkUtxuMwwwPuX0kSiKM\nWddxOM1x+yE5WQ+PJuhcpd6llec2MQn7i4/xQzdcnnKsPuytzJgAeDY8wdEx7Ss3PvEyMlab6J8M\n4NXpWTw8GeFoQuX6k0ETl86RA7Ky1NH9fEop5BxIREkBwedWORprRC8AXUYUQqDVoPPGPdWLtEgM\nR+9mxKgwYPHaPxpP8NY7tI++9c59HG7TvN161sPokPaG44MjbJ2jVhgTNr76618FAHTX2hqRKlCe\napEotf7uL2PvvHsbr7/++sLvPyvzndmZndmZndmZndmZ/RL2sWamojjTgjidpTbAKLNZmCErKhI9\nhT0m5ms0fJTcyHwyHmN/RFFDEJdY46zK4XgCz6ey4PlLF3EyoKban775Bt58g7Tn8lP8O1E4wto6\nNaUlaQGXG/nG4wli9vAHwxHqHE1Oghnevk2okX/8j7/81DHOogCeU0XkLtImN8pbNhjIhtFE4P4O\nRYv3dh5pHTupbLByAuqNEpZF5as4UPitf/R5AMB7b7+LH/zwe/Qm08baeYpEbjx7DWHCUerBHu7e\npgxBmqfIOPqYjEaoNzg6MB3cfoubJ50jXN2cSwj8Q7azu4+1DeZvElJHVVmRAsxDE8ZD+DVaWqZj\nI2F+FDvvwgI1iF/wruNa8zm6xsMST+7Rtewf7CHdY26jaC7ZIh0H4Gqrd8lE6FMEU8LAQZ+yHmEQ\nwQTz6CgfggXBslSiyBfPSZ8mN82zHBNuYg2iVOfj0yTGT16nzKfvAB6T/Zmmg3BM70eWIE8oQkqi\nBP0juuY0T7DJJZO1zXPorlMEn6QphicU6e49eYxwwnxrQujMqhKn9OPm3JCsBbd4+STPDLTblCE4\n7u/DbVPEabkxBhPiSfPrHbguZaNmE4V6ja4zyI/Qbtd4LArjlCLgzpUleI9o/sNRhKvLjMgb5fj1\n3/w0AGCzuYT/+3/7YxqvFEiq7IjM8fzNFwAAq6urcLl01HJtPPMcvV7mBvJ88eZlUwgtZWSaQvPt\npHmps7WlUvr1olBayqiUJoyqXNtq48pFioy7S134TCJaXzkPweCKblkiY7RVZ6nEay/RPDQf7YNB\nilAlUHJJaRaEODqm9WCIEg6XU7M4XBh15LcaKLipN4szoEn7jnId/OCIEG3H+1M8t0Elrdl4hjaX\n9jrNFhS3AnRKBy9cpsxkxzXgc/UgRgGf9QMdYcLx6PUoz3HEZJ5N14RTp9/1JfCkTyXZ/jhCs+Ka\ng9AZH9tx0ebM3iLmOXU6NwA8PD7CSxHNt8IILncmCAmIgl9XAgp0PUUhdKlWSGA6o7/v3D/EepOu\nzbYNTCeU+fQsQ8tUpUmMmOVz4rSE6dI+NJvEeP02tVZYbgMsdYjjIMbRe3cAAFMjR/P84hnU73//\n23jxxZcAAJ125xQq98OZ5pBBHFkcaBBVs9nS4JQ4Seb8aIaNkMuC95/sYzCi+3JpawtNbn+xLKFL\n33GikDIH2njwGCfMm5cmCQoumUkBPH+TgDY3rl6B4FzMIg0iLuZMWuPxFPceUUn/nTsP8fAJPQdx\nnMK0qmxpiIAza+H0ALvbFSGnj94BZX27Sy7kaX4uTchZwmCU/ke3+bP3hS98Gpsbv1ij8BfZxyt0\nnGaQzNaaZkS6VVnFMVbzXWS8QXluEx5rHB0d7eJoSgfTOMgx4jKcKlIoPsQvnNtCjXudbr18S5cK\nPMfFgMstewe7qHPdfTKdouCFeDAO9FnUaTaguNS4f9zDvfv3Fh7jYX+MOsPqi6jEhB9gq+HAZxbo\ndFZCjQK+nm1Y1XLMDeTcW7C81kLOiBOztPBkhxbxv/03f4h3f07OXWKU+MQr1K9SWA3UKkLUtEDA\nczscDjA7lbK9des1GtfBY7z7Fmnz1T0HO5+5tdD40iTFdEwT5XomvEoANo80YkuKGB6XtMpcIAqZ\nNiBfxtUO9WltOptos05fbqQYVf1hg0DreAVBjoBT7UaZYKXFZTK5j5Ah5s2ghpTLfEUZaZ1DAUcT\nigZhgDhZ/OEyTQsFo6TiMEKWzXtqqlKKUoU+kPf2nyBNq76ODhKeh5NeT1NBjKcxXIfW5tbWFrpM\nqqpKoZ3sEiaWV+lQu3LpMh7epc35/gd3ICokjITeldRpNmB8NHxN/2gE26vEvydIcnJmR5M+OssM\n8S8UZlO6L+srV/DkCTl3eSExiSrCT6F7MGZBgBvPkoOc7Q7RUnQf/8PP/BpKdmb/4q++g5RZ3hPf\nxG5AzuMnWjfwxt++xXMb4ctfovXYqrtw+D7mAsg+ApO9KIv5vJnQDnKSFvBPSVEqTdpZQPD+JB0H\nBqOJhO1CSP67tLC0Qv1I7bUtxCz0HWcFhEX3V01jtD1ak8tNG8mQ0anCQVmVTHwDtWUulZQKMZd3\nE2XPIe1PG59tIRjS75epwMp56pdRfhvvvEHtC59quei6VWndwwZTphgmMOZ7UloOlrm/aaPuQ7HD\najsCU34um14DrS59tj8OkPLzsbLcRMj9XvsnAzw8YjJlGLjK5cKd/gATdoI6a+fg1BZ3NLKgxHRC\na+39/Ahv36GSkF2amvTUUhZUxo6GFFCM9sqyHAk/u5bt6PL4MEzxQy4tDYdTdHgxtH0LNdZ5TYsM\nGZcIJ9MAYPWCifDw9iPq63G8GSrX3hACivfZwZPHaG4tLzzGk+NDzCZUduy051Q/hlDz8KgUCFnP\n07NtbJyjey2lwJh5NSaTCdJk3ntXsYNbro/xhO7ju3fu4tIFCgyi2QS9Y+qnO3j0AQJWJBnGJaZ8\nvtqWjXqj6imzkaR0bm2d34Rf8xce4zSIce8eBcnv3d/GHmtsxkkJw6h6bQtYJpMQ52N02+TwTvoC\nsyldW5GFODogR+z6zctIWW8zKxL0+zSW0XQGs6KsWN2A8RFIt09bvVHDS7deXPj9Z2W+MzuzMzuz\nMzuzMzuzX8I+1syU7XjwOMVomA62d8nDN00DvkfRRJKnOH/xEgDAtQViTlX6NRcrjDIZTg5w9wF5\npyUEXG407vX20WCNsYvnl3DhGcrmoFCaA+b8xQsYjinCro2n8Pn1g7d+jiZHOoP+EVyOhrf3dyE/\nAjJgfeUZpBFdw+aWDeGd48HXMWV9wCQv0KrRdy45TSy3KYrJ40ynlg1DQvo03s++8lmMjiltP5sO\ncX6TypT15S5uPEON465QWOHG+jLo4te+9KsASK7kx2+Szlme5xoFV7NdbJ2nMk+j7uCnb/9wofGt\nrdcRcHNlkgGuX5GGKkRMRFpaEQzu8rVDH1sFRUJXWregxhz51y10z1O6/+69uxhy5JpLD7Og0sIz\nUHIp4tIzl7C0Qn8fRbfhNSgKDIsSGaOlLNtEjbM/aaLguJW0kILjLt6cLaUJWVR8QzmklumY67hJ\ny0G3S/fBs2sQHE22mzWMFF3/OAowHtOc1Ot1rG7SffZrdcymdM1ezYbN2dEoz1AqRg46Hl56mbKI\nhRC4x1kqSyooTk0VIIkVACiV0E3zi1jNt9Fs03we9O/j8RMq7U3GJq5VYIQ8RZlTVDcaDRFxM3Iu\nFBrMyZYkMRRHzMePTmDw2ndiA6/dpOjZUxaGXEq5+2gHP7n/mD5bSLz8GxT5feGLv4rvfpPK8qNB\nDzlLJh2OezjPJZaj/SMc9HoLj/E0CaqUYi4VUhS6lCKEQFGlBg1Dz6H0XAxGFJ079RxT5n6aTA8w\nKWhOLj73EoxKdqgUeMQklfuPd+ALBoCkIYyKc0hIKC7d+J6huYLyXGHEQJVaq4OYn4WnWRxHSHhP\nqdV8XLt+HQAwPp7gQo2++7dubsJiTcVgOoMlGSFlCL2vHQQ5jvqU4bKVgsljakoDDoNjYJnwuZy3\nXLdR4zJ1zZU44AyOmZh49hnKrP58e4wPduhevTs4htViXrjltq4eLGLCzmHz2TDNFf7oz74DAFhr\nLWG/T2vqeHSCzTo/r0mGgqWOMlXq+6yKUmcpBSSGTO78+t091JmDbq3hoV7xN5lKA4n2j6ZIuc6e\n5waChN4/jgNdZjfkXO1NhSF8sXjJPZ6McLxPWZv19csYMz9XzVYwOTsaxxmGnM2ZDIZY3qR5zvIE\n4zHtJUJIzd1XG3ha1mo6m+HuXaquzGYzWPwcHO7uYDSgTOJJ/wiK18MsiJHwNViOjXqLysTr5zbw\nta/9UxqvMD4KmA/f/eE72DukDNoszqEkPUOmlQNVibZMkTJnXVGfAIr5DIWl+xlEWSDmcuR0MsVf\nfOv/AgA8eriHgDODreVNXGRd0HaziwtXLn+EK51bqUqdzV7EPlZnajIdI06Y9bzI4HFKNUkjhFwX\nT/IQe0zqd7D7BCfHhBT5xCdu4OrNalIUhKSFcjyYfkjrqSJqvPdoqjcxSxqwGdbTatZ1Xdl2XbT5\n9ZvXrmvW60dlhm6H+5WyDC89++zCY9zcvIrJmK7nwiUfL3L9XpYSWUljVCJHWTFsGzbqXtVbAGQV\nO7AtUOPXN1aXcLBLC+6/+pf/OVx+XdombJ5D07bR4nRsdnETrlXV/iP8xlcJ/RCGKdKMFmiW30TB\nDPTD0TEG48XQbq2OxHhGD4VjODBtZqiPU42gCFWErE9zfCnfwgtL1O/1zNINHGbkyJ7bvILDPn3P\new93MGIG7liZKNmRrbsObCbhdFwPwwE5cWbTRJrSAW6IHPVGRWDnwqwOSVmgFIw+KzPYzuKOhnEK\nGUc0dVwGOkXwWK/VqV8BgOu6qPHhtby0iShgMsbaEiQzRfu+D58DCSWg71ucpigKmrciz2Fzn8l0\nOkGrRU7EjRvP6TT3ydGefsCFUpqxGUJ9JIJfS2QAaD2OJicIA7ovVy69hFaTSpD7+/cxZdZzxzGx\nxgixw0Ef4PU1HU0w3adD7faP3oMI6N79B7/zNTz/yq8AAMLJEYwmPdP15XMIi8f0ehLDsyiA6XSX\ncJHRmuG7UwSMHBNWif6YeyrSCNtPniw8RgXMEY6igMnPnEIO8P5hGHOSvkLakD6NcZQK7LNo89Vr\nV2Bxj8rh8RAnj6hn40thimpZDftD3Hn3Nl3/6BhXNuneOZYJix/ptASEWUHUC4APa8My0OV7rYSE\nZSymuJDMYpgxjWNpq6NFd3vv3sNvcivDctPVe8rRdIqS+0s900KLD+peGaHLfVKelJDsvPi+h4Rb\nLhJbwW3SWvY8YIdZ4PcPUvTZ6dzottBhcs7eOMWDJ7Sn7IQz1Cr1B9dGIRcPTgsEWuDcs12MB/TZ\n779xBydMOtr0FcAFt7wQKATTYUhDs5sDOYSojrt5QTzPCpxwADsKQt0m4DgSOfd6ToM5TQbx5zLF\nTVGi4J5RpZR2xE1pwmOx6EXssLeHZSajLZWJPdYcLKIeNjYpEO0dDfHoEZVuR6MhsnvkHNmdc7Br\nFJSGQYC//RsmvhUmTriXdP/gEEdHdC/SLP0QStVlx4pE1mlWglmg9e/CMMRkOuXr3Mfv//Pfpc/6\nji5xLtIzNRjGEIzqtpwCSCqBaAXw/cqQQvH5hCLG6ISueToOsLzK/ZqTSMNdR+MBXue+6Ld/chtO\nh3o0mxsZhiu0v9564QCrm/S6bdu/kNH872M5/6jEq2dlvjM7szM7szM7szM7s1/CPtbMlBAFLI5o\n42gKz2P9qijELiO40jxDf8QabMFUk/2ZDx8BgiKIdncZV7nBz7VNjFnmQKUpag5F/0mR6eblLAlh\niKr5NIXBUallWSg4Al7ttjFmHo9LW1s6E5BECXxv8bR0oz4nakxLwLLot2wpUbMoWjEtAyVHhaXl\nweDsgilKWK2KIFTANsn3T6d9LDXos5srS5AcYUtDzmMsAS2F4XmOjrBadR/n1ylyKUsbYPLHQkXI\nCpqfWTTG8ehgofEJI4Zf5+jNlRCcFaz5Huohp7YDFxeaVGJ9rvki/IiyfEZkABnd/795423sMpng\n8ckJokqg3fO1rE8WR6j5FJXu72zDPseZvbrSTbqOZ6LBSLQ4kCi5ZGPaCSq58CxNYWHxJkTDMGFW\njchSznW8MI9iXNdFgzOBSuVaLmM2i1ChcM5tbiHkBl4hhOZ06XabiFjaZDKZwmHVd9M2TmnGlfoa\narUV3HrpVZq3H87mpLNGoedflSU+SiBV92w82HkfALBztI9VnzKARhkhy6qMyRZsfkYfP97G0hI3\nya4u4fiIeYOiIxhcwjHbDRwe0No/d+k66izHcXDcxx/+8Z8AAN595y5qnOVZv1RHc4Xm7cn2Qxz1\nHgMAyiLHOz/7AADgtyU6dXrWV/1NOMbiEX+92UDJZLfjaQqDn49SQGf0pAAEZxSiwsSQASNHwxEs\nzki2WzV0upTpaa6uw+3SPAi7himXVdI0x+oyZfTctSWYBe1b0hcYcjY2jXNYJiPuIKAl+FSpsx15\nUQLFYojFq5traF2lUn2alxi+R9mK5Z0Jgg36ncE4xP4xXaMwLCwz0rg/DVBniYz15RYunaM9ottp\nweF2AVOUGLFOaqPjwOKMaGjYCLvUvvD9H30bNW6/cFureH+P7v9bhyfYZk62mQ10Vmh9KSEwqdbv\nAua4JcDEx46bYv0C/dbewxzPcNbxSzev4627j2lcYQnJaCZXSBQ8ydIwdJZSilM4uSKBqghFpUBa\nVFxaAoWqNDMLlEUltSI04iyLE1Rbg+VYOsOZ5wWyZLFSLX3WhMOZrFJI/OhHlF2a9R+gu0zzXMCG\n5Otf2lgD+JyL4hTf/O6fAgB+8L3vYcSovSAIIRktbxgGLLvSbfUw5bPNsixd8iuKQvNVFUXB6GDe\n7/iI2djcwCuvvsLXU+W1FzMhc0jOZVkq0wAvlScwOTMlywwSlXRUAS7kwLE8WIz2sS0DDS4Tp1kI\n3+cMVzLErE+fDXILty4RgOXChQ2dsf/oEj8fzT5WZ+rc5goChsiG4QR7e0Rj8PY7b6PHwq/XbtyA\ny5hX02ogGzIh2WiKuw9o0a+PJ7hylQ7rS+dWEHCqOw5DOHwAzcIEKyu0+IYnA0RcZxVSIucb6dhS\nH4KW66DmzftDTL550szw19/6KxrA//g/PHWMUZLCZ2Izr+Fr1sCkNJBy7w0ygSJhgrRyiJJrxkKl\nGoGRRxkch8brOyYkk1cqw9T5REua2kmUUmhIdaEAyeUE0yjhWlXJykPOzqPtmEh5J5hGE6QLVsCT\nNILvV9DjHBH3bEhhw1K0CT/jXMdnNr4IAFhGF8mADpY7d9+F8Ok9qSy15l2cSaQZP7xGCYs3wyCa\n4TikVO/m+SXEkjbhOEnQWKL7rMpMawVmuVtljAEj0qKuQua63LaISSn1A1ii1JuYJSUEixFGYYQg\noHF9+rVPaac/ihI0uBySpBEaTXKUajUfgjcuYVjoD2lcCqUWRo6zGKakTbXeaGinxrYlul06qFeW\n17HDa5kldfma9Z8L2f3tBxgw8idMbEQmrf3D3hB+zKjJwMVSt+rNeIKQe3kM00RSQd1NwGX2/Gsv\nXIal6LNHx/t4/20S5P7DP/i3+D+/8e8BACfTGOcuUUnj87/2SVx5YYt/q2p0/yUAACAASURBVI9O\nu4JauxrKHcym2gFv20tYWV1aeIyWacI2K6FjwOYgSkmpRbYRh1r5dTSJMeR+t6xIsbJMz7FtCDjc\nV+MvtbFxjTQwCyFgM4XK2tYF+FzqFVmIu+++CQBISwWDD2UjTeBUPW62i4TXkhBKC7cDi2/6NzaW\nYS9R+Ww8mODCfS6P15exv0tOjSMEcj4YVzdqGDDcfDaLUbgsCp7lCBlhWTcNREx7IC0ByXXM2TjA\noMfUD59+STtQBnK0eV+7v93D//MuKzLIErhEDujLn3gWdRbGjooE6iOU+Ty3hjLnvQ8KU9Bz88Uv\nd/GaR+WbyfEJxBVap0dhiu0hXf8gKFBWQuyGoXumqFxP319kqaamUYZAFZEoQPcQ0f9WQavAJKAP\nB2EMj8ujRjF3jrOk0JqJi9hgNESc0rOV5jmODimwNfNII8n3emNc3aC94dYLl9HosJOlLHz9T75B\n39M/QbfDYtStrlYvsBxb9+JGUQTF2oKFAPKKAibPNQWCOsV0Xv0bAL74K1/Es88TWjcsS+Tc39la\noIOiTCfII06MCIk4pntUJLF2+mRewDXmvyx4T1rduoCqmLjccbDCWrlpNMbj+9TraRkSHRbcnqkQ\nZUK/NTg6xjtvc2DWbMJv0PMSxgk2V2murl8+D8Oca5/iFN3CR0FIn5X5zuzMzuzMzuzMzuzMfgn7\nWDNTS90GpkxE+Ojh+xgOqbl8ZcmFX6NoNY8nMBlBUvN8eDXyQuMkg8kEcsPxFMc9ilCWVtYhXfIJ\n17vrCGYUMUfTGZY6FBnVTImUU7kQAjPmATKFQN2vslFz6ZqlbgdJSt77/l4fo4O9hccYZhJD9rrT\nOIbBaeNCKagKISakzr5YhoLkhjoTBYpKs8i0dJYli1MtGwIpITlrVgqpy04SYo7sKgUsm8aSGQL3\n71FD453b93SkdunKZbz0MhHFNdpLOGSSv6ebgZKjmTRK4DPyx5I+/JKyBhdrz8OZ0rw+PLiLwYjK\nDEvtFTxzlaL6Vlri5CeUuQjiAAPmy1leXoJrMwplfKhRHP7zl9GXtF4ylWhds7yIoFjLLElzGJWO\neGmAbyGkacJ0PkKEYQAmR962a4PlzJCluUaHpUmMBw8oKrp16yU0mzR225qhyZlJQ87XWhgGSLhB\n2LQ9OIw6LIpIl4UNKVCv0+vTIIRigIBfbyPmqLG+vAKjx+sxySD5/ithwPwIcjL3n+wg4MzXcajQ\n4BL0MJgBI3q2Do8C+D5FyVvnNnBhg7LBo8EINYclmYwCkwFlO1qrK3jh0/SeMBng4QeEQHx49z6m\nDAzJTUM3r7c6dSgwd1UygQQjiOwprl+jrMP2no00oTFOpyNNsruIRUkOwUjfzvIS8pQlbQoJCJpn\naUpILqdbrkIR0D5hGhKrSwxCiUJElYSTHGKFM4mpUroFYGXrAmYMzBgNDhEwGgpSomREsl2kcJn/\nJhYAuORnSEMTIKIstd7f0+x7r7+PlEsn8jjG15qEwjRbKcq00jRN4PMed3ASYYUzTd26jwbz77Tq\nPsD71Gg81sg117MhBCOtZonmUsv2x/j6n30fALBuuWhxZm84TvEaI417DQv3GX27fGELwuWsR5Zo\nMtRFzJQWSi7PmoYFnxv1l7waRMqST7Uurl2me3VFRXiBn9e/eHMXe30qaQkxzy5JY071WI0JAKQw\nNGeaUgpCzVs0CmP+/mlIcyUdF1MmalW2hZL1Ni1YkB9Bf/DoeIA9PmMGoxMEzF3WsgsYsuJlzDVa\nbRLkuPuEyux3PniMvX16XstCIeeskxQlxrynJlmClNdgnue6HGnbpt4/SqX066dNqQKtDu1tv/M7\n/0STyw4yhbyoCEIXGGSUQzHIzLQAydqFRpZp4EQxG8Fhst4iTQDOlq5cfg5S8HkmFEzea6ODAXYZ\nQeu7Hq5cpmb919+8jW/9xZ8DAD5460foc5a7vdLEjRcJPbyzO8WVLcru/ev/7r/BMuttZkkEgysa\nUjgfKd30MTtTTaQJlSvS+AL2bNpge8d9hAw9/ta3v6OJ+dbObeFf/Mt/BQDIpIWMT0c7m0JxGu/9\nd99BZ4nSdSfCxJRhokWWaaoAU6Vao2swCzQho2FJHPboZnzw3h1kvBCXV1aRMBlYHGSIgsVFckUS\noOAHLEsT3ZdUmvPS0WQ8RZxUi95AyZtzu1nDyhLrVqUxcjmHb1cL3TAMKulhrplE78Fcm6iU8Hjz\nerizg7/8Pm18SZKh26H5f+vOHUQlXdsrr7w838yfYmUp9EMkUIcL2jzPd59Fa4Wg2Q++9wg/+RlR\nLXz2c6/hpVuE5rt+/Tk9pp/+1Texu0clgSgN4TKCKIoCeCb3DRkCNvcSjFWAXkHOlK0ioODeGVlo\nNEiuUkiz0hXzkVbiw2aOpFj8HkopYDsVdNeErKDf0tBIoVIpHB6SA3r37j08++xNfr3EiAlTx5Mp\nJtyfYBimZvwPglCn1KMoQsCopOXlJQ21zvICzSYdEEEY6mDAsGyY1WGUCpQVskjgFHPy081zl/GQ\nN+TISHE/pJJ7EeZIE0a35QITSddvK6DJSLf+YKjZu71ajNGQ5jbwMlgxzdu1SzexxCU5v1bXxKdO\n3UPOJYdgMMLqJjmeKBK9+a+tuki5dDsZRJAMo3ZaHoxi8YM4ETWAnX2v04I15gMlURgxNYUJie4q\nBWz7O0dIWePNMaXW4w3CCCFfc5oMYO/SXE2fHGrG9F/9/GtaaeDw8BCKDwLXdXX/olczcWmdNvCD\nsMB+wGVTaYCnB6aUWkHhaRbbJVJuFzAGE0iX9pHuchsxl2F3hyHWuJxu2SYabXJG2kaJLrOAn1tv\nI+RSaq3ehM00ImmeUi0IwGE4xiik6/3mv/tzHB5QW8bG5TVMeBuSvgOfg5Y3Dx7iyZTW+GGUaCqF\n/vgIMZd78F8sMMhM6Z42iTmNxV48RJudwasXrmDGqNOGv4TDHgUnpswBdtBVIVCdjFJaSDiQSJIE\nNvcToVS6x0oppQPVslAoVVUKjjRj/iRMcHxI62JrA/CNimzTAdTiR2sJA/fuE4lo8Vd/jt09ItCV\nSz52jykYe//uDkY9WvuPHj3C9i49l4+3e5quJUkS7O/RHlmWAoLXYCmKDyF9jVNae9U+l+e5VhdQ\nhfqQmEKNyTk7nU4lt45MCcQpv8l9eqAaz/YQTmmfG+cpTjh4T5ICis+/LJzCY6R6c9NCzeJkiOsh\nTyvN2lN9wkqhzGn+620ffIRgfNJDWpU1AwMxn/eRElg5pt/afayAnOY2yebdXycnPdQ4qdJsLc7U\nD5yV+c7szM7szM7szM7szH4p+1gzU7Wag0advL5XPnULN5+9BgD43g9+hPGIpE0sITELyWMc94/x\n87dIY27r6hWk/Hp3qYsrlygT8N2Dv4bHnvn5Zy7iR3/9BgDg8eOHuHKFSgWjox3MOLt0PJ1LlBRZ\nhCQmb/mwN0DCTcQn/SPdsG5JG91We+ExZkmCCfN7DGcjhBUa0fMQMbLr4cNHsBnBtbpxHmMmThsP\n+/jKF78AAGjZlm78w6n0qzydmRJyruNllCj4/WVpII7oO//4G9+A4pLV5z73eewfUERQQwO9I8rK\n3Xnvto4+nmZJnOiSFmwHeUBR9N7+EVJuxlw914YRUbnnxRc/g5pHHn6UStzdJa6U3d4Jcl5+ne4a\nopDlAtIUZUlrpLu2hdymiDOxjwDmIDGMksYOwLJcXd4s8hwhZzTqNRtlFUcphTxdHF0jIPQcm6YJ\nkf5deQ/DMDRS78H9B9i6QI3UzVYTiq9HCQONFmVz0iyraIWQ5wUGfM/39/fRYlI827F1dnR1dV03\nZo7HY5hV8zpfEwDkQnwogizV373Ov8/iYQqTo/wrF86h36Ny2zQBHIfWZlkmujl7Mpvg8WOKnifj\nGLOqAb3mobPG+ol5BsegSNdwLTzYZ4maNKjkw+CZEgVDN4cHQ1y+TmMcDQaIqyyL6SHnZ7QU0DxD\nYRigZi/+LI7LOqpqWxYFyASFrtI1YPLacE0LFnN+5VBa10oJgSmXJaJMYTCh57i+3ML9+7SGf3bn\noc4orS11IYMKPZwg58zjcDyB7dB69oQFQ9Jv2VYB26p046TOVKZZBstcbFt2TANgmRvZcCBB89px\nmzjhCL8YpVpSpdMyETIybmu1hg5Ly/hLTciASz+WgMtr3xIS42zOwbTXY722IEW7RvfE9k2kXPoZ\nhzm+dch6n8UMVoeyUYGaoORr6G50MewvXgITuQuDATQoSM4HAKAy9Eu6udneIzy8Q2WyZy6tIuAy\nXBJlutyWFxkkZwiVLDSPUpplOusvIMBJaEhpwuAE4cp6vZKUxd7xDCFntYazAN115oJ7cR3HDylb\nJzHPGC5iu7sj3L9L2c7vfP8d9A4p6/tTCYwmnM2ZhrjPGwg99hXStzlvoC8EDM1RJjQiryzFKcmk\nUu9tAiWEqEhtDVQCoEWhYHL2xzTm2oW7/T5u8vwkoUIY8BibT0dKbz98F8csNTSLgL3DAf8WsL5M\n61CFx/A8ms+G0USD+QP9nYdoevQet1lHMaO9KgumaHHZumGWupzuWJZGYOfCQM7Z7CJPUXBZVxQl\ngojW8xs//RnOrdOZtntwH22uaC11t5BxVuuVFz711DF+rM6URAmH620qy7C+QgiML3z+S1hfpfT3\nF3/li/ju90nI987tD5AlnKKbBAindLAGjoUHDwjW3W35yFmIdrD3AB4zoAfxTKPOyiLVfUn/5Dd+\nDbffo+7+//fr/x4Jl0/Ob1xAkjAaB7muoT568Ai//3u/t/AYv/7dn6LHaeYim0FxulEogZQPSqUU\nvvzVzwIAVjeX8fgBITZ6xz18+3skntv1/F98OAr9H0ghNPKnNEqoiuFXWNjfJbbcuw8f45Uv0G+t\nnzuHEdfj6w0Xly9f4i9VWFQkN00kXE6vKyvD/hEdLON+gKZN9/OFy5/G2meoXFmuKrjMYP3O7dv4\nYJv6jIRlw7Toe6LZVPcnuJYJs4LxtmqIa/TgjM09SC5Ldptt2OxoOCY0oR5KoR3QKI5g8SZs2VJr\n5C1kYk7rl6XpXPA0KZDlc1b16gD84O4H6PB6ufXyJ7WmWwmhN/BwMsXefo8/C0yZCK9QChaX7dIk\nxZUr1PfSaDR0mbJer+OEna84juckc2V5inBuTii6iK3WHGTVJpjEON+i5299qYVDJsptL3VRZwHn\n0XQKg9F/s8M+9vvUpxHFHja2qEzW9euo12gjeuf9+/jrv/wxAKLqaK2wE6Ry7fQ/2TnGygHN5/v3\nH0KwVmOjlWko98pGE2nFjq8STGaLA7J7kYHCrEgYbSiP0XxJjDXu7ZFlCSUs/t06StYkK6SJktFE\ntfYSpkxlsdFZwYz7cMLJBGBUYBSkWOL3Iy80SaLl1dBtM/Gs9FCyM2WYOUyDCTHTDMMBrfPdg0M0\nFgzebCmhmIgycgzE7AhOgwionlHPQRxVEP9SY5MsS6K9SiVWx/WQsi5bu17HgJ3CWZIg5r69umei\nW6fvjPMc17o0pgdxhK8f0B4wsUvs8gElPBN+rXKsDcQ5zetaexO1emeh8QGAI00U/MwVJTS1hFHa\nmLFDZEYRPtimwPDN9x+i4dG4xtMEpdY5lKggfCoBHO7bk3VHl7eEVOhyObK93EBrjRzS9YsrKCJ6\nz5P9Mfo7dA65rsTnv3AeAHDrk5fw7RMWeT6eIJxViNun249//A7SiOYnVgJ5Xon64kPPdM7OTlkC\nvl9RZSpE/FlAoijm79dktKeC8bKE7r81pEDJRMumKFHw+SSgdJDu+XV0V6iNxqu3MGJWiyhUc+T0\nAra/PcbeDjmJYWJjwo6Y7bkw+PqSOECc0J4dhgEMh8vg7zzCDQ5WL37iWa0/ONjbRgHupcotjLiM\nX8LSfVyJsmEzMfDaagtpViFD+5rg+xt/8S0IRet2ddVBl3Uqs+QNnAzp9UWcqbMy35md2Zmd2Zmd\n2Zmd2S9hH2tmqshytJnocDoZo8/R23g0Q8TR58WLl/HPfpei5OtX72DCauplLrG2Qt36k2CA1jmK\n3tLZIZo9Qqs5H/wcMaf9zIbE9znLkwVjtNpUStl58D6mQ/KQLdPGCatpD4cDLLPK+frGJViMbuoP\nprDcRQjzyba3d5FqaZwARVbxaaRIuYE+zzP0+hRJOb5Ev8+aS5Mh7u1TGU4KY460OJU0EqcQfKeb\nCoUo4TJPlipKRFxKURDoc6YsCUKM+jT2zc1VeBzdCAAZN/I9zTy3jVnVSBjuI7cpK1jf9FHncsmR\nuY27h1SefXz0CEsZN2fHLhIms8vTBGlA3xOOBnCZiLQwFGYp6y7WAogORQb92T7qTPbYaXTRblC0\nkSQzKI6ufFcBjH5BacLmsmOcTheW6AAoAi64fNJe3cTaRfotYXu6sbgs55kg0zRx/fnnAQC256MA\nNzfbDgouCVimrRtXe71DhIwOW+52NJnk2vISmsxblMQxFCMv292OLnWkWQzBGb24FFA8n2WWw3YW\n59K6er4D3yQwgt1ewqX1zwEAzncvYLxDmdts0kMQ0j2auQkC1su77C3j9gP6rceDJxgIyqosXexC\niYqvqoeYm3+9Vg0ON6aXqUSWcQYlFQg4CrfqAjWb1k93pYWIEbENJ4fgZt50lqO3s7g2X2n7cFie\nx7UEcg4dZRYD1Vpqd9FdpnaAnSCFXWWVlYLk6zl/9VnNj2fX21hjkMMzl6ewfUaXtZvIh1RqOj4Z\nYMrN5eeWVmDZtPY8uwaTm2ptq0DK4IR7jx5je5fRXIMh2p3FMjfCt+Bw9jJuWLhzQvehCRNLKwzE\nMAU6nHlZa5lgoClGkyl8m7JzKktQcig/GE9xMKTMi1AZui7z9iUxTAaGtDwJz6Dv/MHxEd726e/2\nWgcNSb9bxCkqtTqV5VCc+e4d7KHJckWLWFakUMzeWEKgSjCXOeAyue/5tQZuss7nN9+8h+MJlds2\nL7bhpPQe13T0Xm/XBV78DGVTpSFQ5DR22zPQYImoeq2pZZ5ypTDkzJrZrKEEzc+nXr6Alz9JxLR+\nw8H6Ft3bh4/38eje/sJjnEwjKC4nKWHoikReKpyWOilPEWlWBKRRFOlslJTGvFJx6tAQp/9dlvrc\ncCxL69FOJjMULB2EsgQ4o/s7X/tn+Opv/zYA4Mr1m+BecZwc97HL0k4vX/zMU8e4v/8YgxPWATwB\nJPPRoeVjbER8/SFmJb2+3w/xwquEvHvu5kt458fUvjM7TnDBpXvXHwMzxejnHrBzSH5AMEsRctWg\nMAxscDk7y+s4GXKLT1kijmlOnjzZR8ul+6vCAtGI7mMQ5Hj/wf2njq2yj9WZyuJUC9FapomchVOn\nkxl4T8Bxf4RGnRb0V7/y69hlxNRwGhCBH4C92RhxySzZ0oPn0/vFcIoo4bSl3cCzzxLB2Jt/812t\nc7a01MWdD2iCTMPU5ZwwmEKu0PcEQYSQU6epkugPxguP8fd+60souD8gK2MoyRtBWWDGqK3ReAyz\noheYjbC1TpvLs5e3UJXwcljaWyKgXqUP9+GHqkJymBJYZiRgnhWY8tyGQaTrvpPRADeuUhnpyjOX\n4LLzg7KEvWAbg+97CKcsxiw7EAwHlsqGxQ9CnIUYR3TodZwWREmHRrfTRdyj61JpAKNqIkoiFCkd\nttJVKOu0WVmtCLHNfRpGijozYZeFifEJPfiFKmEz+sk05j0vpZLIuISrihx+vWKWf7q1uyv4b//7\nfw0AuHrjJnzWM7M9H8L4u49MWUKTvv3Jn/x7fOfb36axt1pIuWknimLYdoUQtNDgU63bbaPOxLFK\nFZhx+c/zavr9eZ5rhmSv1sLv/cf/GQDg4oVNbN8jx+ebf/p17O4uxmIPAMeDCV669hUAwJVnXka9\nSYeClSuMdkiAPExSxPxsSVtBMmrnxJji1icJoXmEL+HhEW1i0ThDnfvpvvLqBSyZdK/3MYW1Rmtz\n1MsQjel+zbIMT7bJub/2XBdrS7RJri77ODlmFJaxjzSng9Jw2nh56+bCY/R8B6bF/T+WhL9Cz5lj\nCe2A11Y30Fyh4G3zwjG6T8ipORn1MWWH12p2sbJM11zrLOveqJXVFZh8j44Od1GGXKrr9ZFVJRlp\n6MPLdVxNyrq9/RA/vUNafk+ePEEcVyWKEqPJYvtNKiQ8porwlxs44etyfQfPbtF8L9+8gGTKvZq7\nu8i5LJgkKVKG2ruejTGXzHZ3e+gwq/RKzcLhkPasYVSgzX1Y23tj/B/HdI3DFQ9bWzSvRs0Fc55C\n7Z0gZHLkPJ/3K81GEz3WRUxAVLJ7MA0HZsmUInmGqpGpl84QV3QVpYTfoLPh+de2sMkiz5OdCN96\ng/Z926xrfVPTtiG5zGvbUv+dZzliLielkYHbP6PAf3Q8wyarUTx/5TyW2+SQJqXC+QtU8qvXDlEm\nixd9smyOIgRyrT1XlnMVhNNOleO4usUgSZI5yespRPfp98tTaPASc71K0zJ1v2YQRLqvCgCabVo/\nv/v7v4/Pfpmf9UGhEa4PHjzEH/3B/w4A+Bf/9OnO1EnvCC4Ht3UnQlyJ2c9mEF3WprUc+IzcTRJA\nsmD1ZHKCkikTzj1zBU6TCWDvxbi4SWPp7Usc7zP9Sl7i2osv0Bj9Gu7/7D0AwOxxjKUV+i1huFA5\nrZPhaIrldd7bjkZ4/x7tSQUsDEeLl2vPynxndmZndmZndmZndma/hH2smanD/SN029xcKQkxAQBp\nVmhq+lkQ6wbnjc1VmJxmdo+H2N8n36/ePoedI8pYmMUmkusUrW6vPYI9oe/xdw406mZpZVWTcH7w\n8MkpDqkQwYDKVP5KV6OJwngEj7XKnnv+FRhWfeExvvbpG/MynBSoxJtMw6B/A1CqRMlRmzjVRGzI\nOTqP5CXmzcWV5SigqsgF8wZFKaC5UCqNJYAQXkpViuofbt7VPCqnGrefZmEcQANVZB1lRN/hWA2A\nUXjjoI+Ax51J4JnnqDzbdFZxlFBqOA1HMD26n816HSLlhm87gH2BM29bBYqcXm/PWrC47HIymWjt\nq263qZEnWSExGlJmJ89ibDCH0fJKB9liVUwAwOe+9BV8+nMUjcGw9LyWZalvyd8n+XH1mWfw7jtE\nRhpOZ/CYx6XIc910LoTA+jqVllaXu1pnsF6v62Z0VZZaKijPMwQs2xMlGV649UkAwJe+8GlU/abn\nL1zF//I//08Lj/Ezz30CHUnXgOMYRU5ZlSgeQhUjHmMCf4mibZUaWGI+t4bdg+LM8I3uJj51hYhY\nR0EE6dPzLbIxHv+M1sDj8QxO9xIAoNawkQT0/VG0j50HLGkTCNz6VCVjcw8NzhxkgYTgrF+rJfHc\ntfMLj1GaAjbvH+12UyNWyzJHwSkUWXMw4IzSxqUVfDKhcu2779xGlFBUejIa4dr1ZwEAfr2Jq9e4\nbC0NVDDFes3DlLnM/M6SJuQchSk8zsYHSYI4p4zOT978Ce7tcgmhLCF0SuHvPqd/n8VRDoPJdWqt\nGmZtbi8wFZoMRjFqDs5zA3E/SRBx9kqlJb7zHmUyL693MOzTWIPZBL6i77w/LRBP6TlbX1vCgwOK\n2P98/xDDm5Tp/ZVnNvEGKPOVRBHqu1zGnyZQV2gt5OMp4glH+FLoRudFrO60oVh/EqVEzvuEZ/qa\nXDHKYyir0uADLC7/lbkDwWfJ4fGOzsSZMOAYdP1SziWZoEydrReyRFzQZ497U+SMHHzl5XMYbFO5\nSkmFKKU1Po5C1FYpm7O03oYhFkcsWraJLGQkq1BaNxKQH9qXq73BMKSWsirL8hQacW4fKvMJMc9U\nSUmcd6BMkMmocr/RgeVwRlQaaK3Q3mA3l9Hnl6MIGt3bbDQgysXvozKbkPxMt1cLfS+yPEfCJeay\nkIgYNStT4OQxgZXGT+7AZA6vd/9yjJhbMFpWiN/8ImkFfucvf4K7t+lskTUL3WU6v1c217F7nzLA\ny5urkAw8MMwCgolALVPCX6JndGljE/UpzbnnN7D35P+nZb5H27uaWHAaBjC5PLNz0AOvSQyGQw0Z\nTpIxJJfJsjREzA/hQT/GPutEqaJEoWiRrXW3UOPem63lCwgTZoZNZzhm9NH93X3NAv7Vr3wV3+WS\nTKhKLK0SGm06i7C+Tpv2LFI4YEjwIhYU95Hn8zSt5EVtSgMGX5tpWnM36ZQOklBSl+2UgCZqFELo\nMh8kkVlWn60g2IY05qXAcq73VhQ5cq6FF6c0pmzHhGVWzleGXM0dsH/InHIZBvfOhFEAi9GWLb+t\nnbg8LeDalD62rCZSLuftTt/CxKayVNLK0WheAgC4DQcz7hvzW4Bq0zUOgjFihrIasoRtMV2FLVG4\n9PrKKhAEA746A0FFhGhkqDcqJBcwYaTNIvbiy69B8aOh8vnWhhK6v+lDpH6nNrTl5WXU6+TEpXGM\njY0Nfg+Q8Ht6vZ4u80nDQKdFm7DruprBuOHVEDD1Qq7UnNrjYAfvvvVTAMDNm9fIGQPw/Asv4NzW\nxYXH2JwOANZDNL0YIVNplOkQBz8lwlVR5lhfI0fJXr2C1KwEjU3YJV3baNqD4H679VYTuWRx7iiH\nmdGm138SwGaHVCUu0lnFmt8AixcgmpS4+wEd7tdfEDi3SQ5Ab9tETL4OUmuKh/lb9I/Xnj5Gt2aj\nzj0hjmdDmHMYuMeHpu972nkpbRMvvnoDANBZ7uIH3/9bAMDe/h5e/TQ51/VGCxbvW5euXYXicl4c\nhRhPaQ5v3noZrS7d93t3b+uAMFUSH7z3cwDA4+1H+mCSUujyCZRaEFcLJEkEwcLYruej4FJkJh20\nuaSyfTJBh0s27VYTXk7P0LEA/s3PCUV8c7eBC0w/0TIMvN2jZ8UogN96me6/UXNx5zH1AXmOA4PL\nhUvHEdoBjbuWCQynNJfjiw1Ihrw3PQtS0H49ORlARYs7GklYQPKzaCoBg1GVwpsLIOelhN9g5KVp\nodmqSuhdRAYTxJY5Ct7jvJoJz2ftUgeQ7HxNwhghw+sNUaJQjAZ2Aa9J3AAAIABJREFUHFx+nu6n\nn6dImUgzThPEHORkUYoaC8CfP9dG76Tak55unu/oHsqyzDRFAe3VFUHzvE8qjmNd5qNWD15HH1o5\n+sSAlKdEng0DHrcV1Jpt8HQiLSWaS1ROdxwXPiMuh9MErRkTnIYFBJ9Pvu+jw33Ii9jzz12By32K\neaEwnjFCOlUQVXJAZTAErZ+WNGE7dH+7nToxogOYDk7Qn9HeP7YcvCWYYuZgjE6XS9hIEA6ZULm3\njTKh4E2lY8Qxa1DWPGQF9+wmMR6C248sBwn3dMryBMf7i6ufnJX5zuzMzuzMzuzMzuzMfgn7WDNT\ng1mMNjdGDyYjhFwvuvfoCVhBBrZRYqnNvDtZgCzjVPvJIXYPKUQ9OJnioE+RcSljuIf0dzgRKBP6\nov5ggiin37JFCo/1qQbjGXo9Sle/+tKn8LWv/T4A4P7hCYbMwWS5EhvnqCm1fxIiDRfPagzDdxEn\nVTZF6tRsURS6NOQ4NiQ3bueJQs4lP8d2dK42KVKd7ZBSai6U0/3PWZahYJY5y7Q+JDlTpYdVqZAm\nFZ9NopEfbmbr7yry/EMNi/+QrXS2cMxIxJoLVCpXtgVUjHct6aG1TrwgS+U6Mo4IC2uKk5hSt7Hj\nwmtQ9GM2LLS3+AfyDDNuTnRyD8eclrAaFgxu3s3jBMur1PRaazQRckOrZzpwbCbtrNuocQkkmI4Q\nR4s3vXqej7yYEypWel1Czsk8Uc5jv6IokHImsJQCnQ5FbK1GHQmns48HQ+ScfnVtGytLlIa2jDlZ\nXhiEkBxBBsEUE24cDqMEE25KLrIYd95+GwDw74oCn/zkywCAl269hP/yv/5XC49x+OgxPJ+j4ZZC\nxnPbe/wQx9tMgHjlPIopl//MCSzO8kilEDM5quXasBWXwfMCyYC5tJII4Ge3TDIMD4mU0PQH8Dky\nzsMmVMyajJsuJGdcH7yXYXhEkX2rvgqDt6nBSYR2fXEyxJXVrs4G25ZEjXUPLdeCw/uBlEDEqJ5c\nCFis7fjMtUs66zQaR0g52+w3mogZoZkrhZL5zpRhw65TWevm1kUYJs1VnOUAZ+6C4Qh3dqgUkZtz\n5FWe5TqLUCqhS4dPM9s2dLYlylJc3qLMwqD0MGR5GDWbIOc167oWrmzRgzabHGLUoHX6Tlbigz7t\ncTZMHDO66jWvg5WLlPmMoxyf/cRlAMA4E0hO6D2T3SNcZMT11nId31llstC2A7MiPq75aF+kRm3D\nNTHcPVpofAAwGU1h8jz5rgOD0doJYpQVH5OtUOOWgdWmjcYqc9B5uW5279Rc+Jyta3RNOHXOKBY5\nUgY2RXGm0wuGZUIyAW1j1UTOWo7eTMLTGqgGYq5yCNvWpM8CAlm2ePbNLos5GMcoYVTfWQIswwrb\nNlFyBlVkJUSlzwoFgwmfSFJqnsmq5MZM14fLpJemY8NmhGt3qYmEeZ0ajo3aOUYyWjZs5mgcHR/i\nsEV7VZoJOBVgKykg7MXbXz776nP6DItSgYf7lFHqj2YoqrmSJZyOngbwo4uxKlAwSjh3BSxJ62o0\nHeJPX/8bnpMIbS7VNU0fJwcEiplOxogYNXtUPEHG2caJ7UDyDzTbLRwc0j6dFtAVsDyOgWzxc+Nj\ndabSUmL/iByZ0jJxwAKpTr2BATNzry+3cWGLkEVhMIPFNe8kTbHXo/f0JwmmMT0A0lTY3qPJdc+v\n4nCH+qQOD3qwWGNKFgHaFRvvbAKTF+LJJESbe0LglBjzBu6aLqKkomo4h9FJf+ExKjmBtLj0ZppQ\nRoVKUrpclOUKEnNERS6qzXl+QEdJpv82jDlcVhSFpkwIwnAuBOzX5lQJpdCvS2lASYaJIoHiB09B\nwTRo8RnC+pDg5z9k47CH/pgWqmmXcJkk1awpjAeMgCwzfTg83HsAwenXC+vn0DDJSZUixnhKbMme\nb8GyWUcvMGEndF1OaKDNEOxaw4fvU+nn6Hgbwq2EnE1ETFdglAIua7GZgg4pABCKkFSLWnmKGRil\nqgCCEFLME+kCGv2SqwLDITkd777zNvpH5FAsdzq6bOs3WtjjNW5aAuvrdPCqPEXEOnFOrQGf9fiO\ne30EAZcT4kwjQZdXVrDSpXnYfvAEb/2Mykaf//IX8Z/+J/984THu947QYoT6WqeN3og2nEcHQxgW\nHaD1pTWYBd1TkU6AkO716OAehkwv0jh3FXUmpcxmUxjsPEbjHiyPnIhXP3sdj0e07mrrKTzu03j3\nhxEk96XYhsBwRIeRgRbyqrx7LkGU0HwmYQm7vzjFRa3hw7Zp7Vm2CdvmdWJJOM5cnyxnaHxh2nMW\naFPg5rNU8psGBQZMF7C0mqDF9C7tVgcJa9etrTchzlObQBrO8PrrPwEAjEdjXL9OmpXjSGHrE4Qy\nWrl+A/cYdr23t6f1+8ryFwvO/iJzbVMHX4UqMeW2gDtGgTeYTuIrm03kLCp7/sIS6twr9l7vCYwN\nWkdOx0RyTOsrUgVsh9bg+5MC/ysrFlxMXHRYUNzr1lC119QNGw0WlF+51MAF7p/aLXLdz5dmU03z\nYfgumkyYuoj5noVq58yQI+PrL/NsLnptCq392PBstFfodVUWWpC77Q41s7zySgxYlHo2KYGEXvdt\nE4qFnaMshWJqD5g2En7ynVKhzo74UZrqMyzPU5jcTjGazBBNFyfQXa/Z2LSYRNaQcPjeWQqoVNYN\nAVi8z5nSQWZUtCwCtu6HsiAqrU4pYTIzvWF7ujQNx0bGTmWraSIKWXevXUPMdEBWmmqqnOydn2pt\n2tzxEfB5XJgm2s3Fnan37vZQslNfKDnXpjVMaA+2zFEwgi9TJRJWnkjiCAXvtabjwGRq+kZzFZbH\nSP6sRM7BW5xH+rO5cFE6tPeEaaJ1HstMwXP4+h0TTo3WcF3aWojbMi1kHwF5elbmO7MzO7MzO7Mz\nO7Mz+yXsY81MjWYhFHvdozDAwRFF863uMl5+jTJEMk/hMjfIaDhBrU4eY6vVhTApRR4kAyQFRdJ1\n24GoUGSJwMpFasLdvHgZRz0qRzU8oM3RU722jZzZ+xrtFdx+RCnnJ0cDLVXRrRs4PqYsQhBMsdLt\nLjzGTJUoqug2L3RDcXkKUVGWpUZ7ZMh0+hMA4rgiijM16qJIUl0SUFmqM2uW7WsttyRVukREpG4c\nhRUCnKCBgqVT15AmYvbkqZl6sfGVIxNORNGeLxzUWHfMCRw4M15OKkWRcbSXOTAzek+85+K5JpFD\nTrMBtod0f/q9nm7YNFMfZk73fzjI0D1PWUrjQKHkdPBmfQV+wRFGEMOokmpCouTX87QAc+7BNmsQ\n/uIZDeA0lxfwYZzM3DQK0zBwn1Xfv/lnf46Cyx55EAKVVIlXh1WnddRoujCZ7NGVOdo1Gm8OBxGj\nF4eTEOMZRYTNVhOv3HgVANButRCz5EkYBDhhIrwf/ehH+I9+l8j1Osyp9g9ZYiqc5DTndjnAA9bm\ne2/3GM9wWt9b2YKUjP6SCoM+lWhP9u4CJv3GOEwxM1heQ8Vwc3ouj/uP4C7TGrh++TzkAX2/21UI\nAsoeFuJEk4423TVMZlyu9R0w8Af93hRgXcI4LvDu+9tPHVtlfsOHYMCAbRuoAJiGCZiyyv4IuBbL\nTmWSymwAclMh4bKs7VgYcyloFoQomTmy026jztIvhm0jYDmL9+/cxd4OX6c0EPK63RtMUXJp2y5y\nrJ3j76+1NWghy3KE08V4pryGM5dSkgZmXGq2VIE3bFo7v3r5Aqo8UJLkuMvSSD9XMaRkTbRMwmXk\naxEkUCFLeLUkvpHQuGtyig0O0g1PoclZkq5l44FH/8fr9QgzvhyhSt1SUOQFinjOc/QRVEhgSQMT\nbtfIRabLtpZpouBm5TwrMGGwxiic4Tw3/2dyLpM1y1Ok3E5RSCCuOq9zCwU/36XlwOYSaxpN/z/2\n3iRIsiNND/vc/S2xR2TknlkrCkChADSAxnSjFzS7p2dI2ozEWSibkaghrzoNxRulgySTSWfqoosO\nMlHSkBfSjNKQxpmejbOw2Y3e0AAaKKAKVag9K/eMPd7q7jr8//OIRAOoKNYYTIf3XRAVePnCd//X\n73cW6SSKYSX1JZYh7vC6eLg7AI6pbVms0WAr6HBngP5ocX6ijbUWzgZ0pgapdQTWUgoEfA6FArCc\nka6URFihc0WkKUIUltUqikWupILke8KTAFe4Ql5TmLLHRlUrSAStgTPSIufsek8ZRDFnSF9/B/v3\nKFFBtJrIt8n6KlstLFcW82YAwO7hAD5nNlshXZkfC+u8KMLCJTaQt5v3rpfDimIf+47EOs2Ns+gK\nSEey66ONKtMKWp2juAi01TMPj5AQfF8m2iLlszaUCRRbr8JK1QXuL4LPVZh6eHSEpYskNOVG4MEh\nMdXGVuLCWZok5AGOmMk38A0ech0s49WwzPWgwt1jFExunvTQaRdFg5fhczbReDBGwkUxW2EVK12O\nsbExgoDTIJeXcfuAMlSksKjzCV6p19DgIosmT+H7iw9TPpcxl2U58oIlW1VczbY8T5CjiHVSjiLC\nGOMOICvzWQYGZvXwUgtnrhZSQvDhUoFy5Jx56EEWBVC1Rcym64HOkXEB2ZoQCGtsClUCVf/RxSoB\noDu8hNWQClTHkwSjPXIPpFJiNeS4iFDCspm74lVgmAx4lI9RYXZQXzdxNmcSxegYy5LeU2mHLrOz\n7U3gcfatPdCImMhRtnLsXCMhAgHQWqaiyqKTo8PmbCsiyOJvhUagqgv1r8CMZX4urdhRvLp/Ul88\nH5ubRDj56pe+hJ075BrZf7gLwcJre2ULv/mb/yUAoN6q4/4tIpI72r1P/BEAuitLOMvEki984VV0\nu2TCPn/hPM5zbaq7t+/g3/3xnwIAJuMITZ7PXq+He3fpAl966cVH9q+12UAsaTx3+/dcpmFvMkaP\naUESrwLPsnDvBdjnDJl+/xDnLpCQW13qYndE8VBH4wfo8CWSoA/LLuB3Pnofwwmth95NgzjlvaWG\nmHA8w1tvZFg9xwJmK4Q09LcP7j5Ad5WE0PXNZdRrj1HXrepDFxQkUsDjyy7wpCuUbbSAx3vF5BlS\ndhclOZCx0mWswJALzkbRFIYVnqO9QwRMButVaxizwHXj/g5EIaCpEPsDprWwARSToIZ+gLBJ+2XL\nWhdbmecaNi1qrX02qg0PKdeMM0I4hchK4BboN//k5Ai/U6fxu3lrF29zEeBguwURcKFgo2BYCVG1\nGgTTAIhJDBEy/UBT4g6/P1+RaHHx2EYjQM9j6odxjGhAlxLGBqZgoU5z5NnsEkuTxS/hTthwoQkn\n0QnGLDRV/Q4yXmvWBwwLEaKiYDkuZn//GIr3X6okQnYLhkEFaVrEkXnunLVWYzQthCCJkKsmhEKi\nzyTOtw4PcNfF/aZIWFDOMwvDMaNKWxdvtwhi4aHPh8n582fw/AtEw7F3uI/3f/ImAKChPAgO16gq\nAExiqbSFKGjJjYFwLjPhstatkDBF5rbwMWbBZJxVMeX106pkLqsuFAY5654aKbKiXmXaQ/cMnQ39\ncQ9L3cWz+cJq1bHgCylgdZExhxkzxVybAeuEwVqjOafPzipPCE9CsdKuc+NqEBoroFHQB0koj8/+\neTb5uX8LMUcNZIwzUGRZ5uiMFkHp5itRokSJEiVKlHgCfL4B6CaHZSlaCB8Rm9SncY79HTJfNFpN\n5FGRxTRCocOMJgkEm6VrYR2y4HeZRECbnmpWfOiUq6jHY/S4MvVJfw/PXaDA5+XNbYxYy1xuVdFt\nk1Z16fwy+ly5/eb9PVS3SJqtB230jxfnmdImdSSPABAUZmnlwyuqyuscKQcrWiln9P7KRxbQ+BiZ\nnJZ0WQOqBL4jlsuyDAVBjbYZMiax85RCK2OrVn/qeLusEcjZjdTOLOqcidSvSiya7CbTuuP/yHOF\nQLNpGB5CXk4iEy7LUGcSUcGXhMzVLEuTDD4TqHXb66iubfP4ZRgz58pSs42YNcU806iyxSqPMlS5\nrhy0RXBEc2UGCVodsnyKMIHocY28uoHfXNxcC8xnxZzWN+ZDgws7lYXF5WcoWPnCxfP40RvfBwC8\nf/WqI4J9+dXX8OWvkItTegLHRxSUfHx0hBpn2myub6JWZJnN/U5vOMR/+B7Vmbx35x7eYVLQKEqd\nReNg5wF+9u679FsLWKYOkwlSsBUBGSqS3FVxmuLqbbI0XbtexRkmghxGCkfMfDppVnGfiT1fqE+h\n++Qqv7NzDReY46dxoYaPfkT76XvX7wEcMAtbw8oaueIbzQoGD1mb1EDAZkgfFRzu0h6Np9YFjq+u\nVJy1bhEoT8AWKq0QLiBXSIm8SCDyFAQHVmsxxiQprDUdGOavq4Qe0oysTr3eIbbXaI3t7x/h4ENy\n79oggM/cYbrehl/t8nsUInalbJx7BtMRu7ODOmLWzvMsQ73O2X9JCmTFvHw2vNBDxpYFm6UImC8J\nSgJMYvmD4/u4yMfXYWiRcXmMwVEfZrPGYwCkU7ZG+R4Ctsp7noVhPiAzyWD9WQD/sEHzeWwjyOKQ\nVhayytxuuUXCNUpzqZFxtprOzRyP0qNhfQmV0jys1NfRH9K6iwcaIZOhqtDDWHCZKqkRR+ySG1vk\nHjWu02iivUR9n0YGlhNbZCVz51kgFIp4Y5N4yNhjoKoeFGfSRSdTJMUZlmXOCmaMnZV4yXOox6jn\nGvstHPCZ3jmzjae++Yv0P959H//mj/8SAKAnE2ww6exaJcTJgCZ1pVJFkdnkmwyFCcdKAc1nWGok\nwOd0igw5B3D3Ex+7d2mfbXaGqPA4RJlGqgtSzQyaM9pSYRBeJN6x6uZZhEuL11g00odm156A80bS\nv7mdxmLmmZmrIWgwx8UopLNSCWvh8572lEQ+V4ZnnuxUzpgCXfiGNtadDUII59qzxkBz2I0xxnFG\nLoLPVZhaX+libY1M2yf3j7GyQodqCIEB14Cq1tuocszH/Ye7mHCGyv2Hh7CSzclpBsVm+sl4gKND\nOsz3200063QpBEHgJmkSp7h5hzLQzq534fl0cFUCD1ttWvTbZ9Yx4OyEm3cfwjBXw/a5LdxLHoO4\nSyjnehOAi2MqJgugSfKcadkgTWeTh4K5WHkuuMBTHkRR68sax35Lk82FTpVBwot12QC1lManEhnk\nCWf8SR85Zzk0cqASzaZ/YhYzvb/11lVwwhy0NpCcstpqtREnJDRF0ylSNpVWq1WXfWjyFE3OhNre\n3sL16x8CAA4PD1x9Ot/zZptLa7cpsjzHlAUray2qVXo+CAJUq5zWmgEdrk84zRJ01skkbfwMAy6e\njF99dB8/iybik/6PwKz2la8CvP76twAAX/nqN5w7yfO8WX0sa3CG98GZtfVT75pt9tkB8Cff+Q7+\nu/+eagXu7x+iXqcx/PKXX0OtSmt25959DNnNtAgmqoooYcqPzGB0h7LzdvdO0J9QL3/yTgpxgdbp\nqGtglmlf9oTAUNKe2MgljE9CU2d1CYbdWzdP7uK73yMFaeBX8OwrtNaeeWYL777FhLtRFVLwJdIN\ncfkKZZcd7mns3Kf2mEBjNKZ1de/+g4UJLYHT80jrqMgmmi9SLSGKw1YKV+9NCB8Vvqw9NauRdnCw\nhwtnLgAA2ivL6PE6P54kCFmQqNVb8IrsI+EhZ9F4Mh0hY5JbGSiEvIb90CBg0kzhhRgcLSZMRVHq\nromw6sMvkrpyDcHC1NTz8JOU5vlOLcaZs3T2vbrbwVucwddb0bBe4XYRyFnIs6GHSoXmXE9TRMzg\nj0jDD4oi6QYZnztQHhTHrXidEDLhiy7N4FfoQo6jxBW6XgT7h0cYGxboPIFlPj+67RA6pT6mOka7\nQmNZq9ThMy2Fqkok7CK0mXAUJ9VcoS6o/ckkRzrmIuJRjoxjDFo1H802/dbRcIATFkK1rSK3XGVB\nZ0iTwjigXP1BCUFBPwvCWAHDayRKc4y4ikM8nMByzb4kz3E4JAEqT31UmQCzUfURFJVEUkFVNECZ\n5EVwUWBngtVgMoHhuYu0RcpezbE+QcZ10j2jHQFzHCeOWieTwNH3SFG88LW/gQtcIHwhSOlckNbM\nBBQSrGZxXl5Rp95aF1eVZMmsbuBcpQ9YASlmccK+KOgiZiTKUghYUxBYaxTHqpTilDA1I7w+nYUp\nFw0mRunmK1GiRIkSJUqUeCJ8rpapX3r96/BY4r21n0ByYHQeT7C+TQG8zWYXKiCNZnV9GxiQFhtn\nhxgweWamZkHBaRzh5ISkycPDljPZN5ptLC+TqX047OHtq2QF+eheHX/ja1TlOk1ibC+Ri6gmc/SY\ndC30hOO5qQQBttY3Fu6jlD7UXEB5kc2X2wS+N7NSTGMu2aCkC1zMcw3lFdI7oFjqlkI5XqokyVwG\nT65zZxb1hIEszJyeQlTwiixXkemiorqGqNLYjtIMk6LMhSdhh4uRIb799jUURjalPKcZkAuFs3e0\nRpIUGSnKVbbPbY4OlyCotldcgsCNO7vIuFxAGAROu5ov02KNheYsKq21sxQoKR05nR80sNSlOUzi\nKWp10vazLEFiF8uQ+o+Fs4JYuMDGwJ9ZI+f7Asw4qj4poP3U+wB8+9vfxj/83d8FAPyzf/7Pcf8B\nWYU++ugGlpbI+vbNb34Dv/itby3c3pv3h1A+l+RZXsfekCx3vdEI45xU1B/c2IGvyDKx1gbGQ3JR\nHSXHYEMAru9MUBg1jamiP6T/8cG1MdpLZH372i+9jiz8MQDg2ec7uH6VLMm5rCPk4NBOt+UyjqRn\n0O4w/1THxxaTUWozwmCwuMvdwjoXep7PEjp833PasLUCcVRYFwQqnHGUGUDJwjVpELI2Px1PHEGr\n36ggZ9d9ZakLwbw1RoaQ7EZSSmEyJEvDcDgCb2+0wirMnLV6OOLzQCrE2WKuhWQUu7qFgR9AFG44\nSIDPglxr3ONA8yEyvOvT+J3dCPH0kPbiO2aIvNDqdYqCQVn6HjRb6mQtQDOk81QnKdKixl+czdwi\nOoHh8xdKOm+eVD6CGgeI+z4qenFtf9yLMMlnNQdTXiO+jrHWJS/EVnsZh0cF4bKHdMK8g6g4rqjA\nplBFtt00RYVdZj5qSNgzkCQpmnWybO/3+vjgAWWxRVnseIsOjyNM+LMxAl5RBxCz+GZlDXS6eN26\n5VYDpsjU6w9x7T0qmXR8dIgLL1ByTZYlACdTNKqh48HLlcLEztzFBTzPczUQawKunXGSIwpobeyd\njBHzeKardSTs9k/jFEbRM6kCYr/YNwFGTMp7/MZ3MeDwDfyD33pkH3WewRahE3PTr41xVjDPC+Bx\n2IKUEoate54fuJI5xphZGpCdhVoIoWZ0VXOldKzRsyQUIRzv3Lw1cN4DAjuzlOm5uraL4HMVpi6d\nPYM7H1Gm09HxAFPOLHn2zBZeeZliTn74znuODf2p81sYsotKBk2YlLNi4il0wpVrrcaI2aH39nbh\nc4HRLDdo8IapNVqIub7Qg/0T/OAnb9HfxhOscZxDqx5iucUHe7M2I8YcDuHhMdLq7Sy9WgiFnMnP\nAj+cFTrOjTMnap27yQsC5UjXMpO7JWGsdu4iIYCATbwiEwjZzx0KMyuMKxX6fJjvRGMk7CoNggA+\nC6o6tBAcn+VLDxWzmI9feiEqTJgppXQLVXoz1nUlfXiclZFmGRLO5El1Dt2ni+W4N4JXkMSFdSi+\nlHSWUVwFaKM4igdoxwytvBk5nQGQcwCM9HNMuGitJxWmXCfMGsD3F4+1eRJ8motQCPGJZIx2znE1\nnys4/56VlRX89n/+2wCApy5dxMNdEnxqtQaWOUv1ueeew6WnFq/N112/ghu3fggA2DnZcW7Zp69s\n4d3btJ/e70+xmdIFaiYR3nuP9u7WM+dw5hwJcZPkBA8fkksuGx4hP6G1tnvoYeMs7a2Xv/gc/uiv\nfgQA+Ms//RAxM9x3Gjk2XiDXHoIUd+6SkJWMq1hd5cuunSHk9ba6toovXFm8j0mcQnFGkNZ6Fgcn\nlBPG0zRzgr8Q0sVneUIhZRqB8XCKQuxRQqHfZ5qHzgomvFZzTyEAszQbBc0xYkkUYcjPJ0kMwRnD\n2lpXrHsynWDILpxqpeoIDR+J1BRRAcijDB6fC9qTrsKCyAyOEhZAp3BxT3dUCs01IYMgcKn/1hjY\n4lLONXSxz6R0rNsIFMBnooKBnhRZexnyIk7KWGLO5XE9FZ+iFhemfK+GtQa7Cxshpgld+JVqgOUu\nufM6lQYOVoqqAwatDkv6OkSDY0dX6x3s9Gm8x9pgNKV1+vD+EcI6zUmrU8MgpbUfyQSjnAlrlUbO\n5MtWZdBcZUPkM/eQgXTrK88t7GPEhSlPomDHGUxGuHuPKICCwMfGFsWSZmk6q+0qpXPh2SyHYIU6\ntPlc6r+ALVx12kLyep+aEQZM3SJQRVjlLL92G2ACXVszAK+HQHnFRxr3Ka0l4QU4eHB34T6uryy7\nOrKep1x8maeUu9tyY3HEVC/FngROe0ytdVGQkBAzahCRY07GmjtrzalA1yIsJs0M5jyHjm7Bau3+\nNs9zl9m3CEo3X4kSJUqUKFGixBNALFq6oESJEiVKlChRosTPo7RMlShRokSJEiVKPAFKYapEiRIl\nSpQoUeIJUApTJUqUKFGiRIkST4BSmCpRokSJEiVKlHgClMJUiRIlSpQoUaLEE6AUpkqUKFGiRIkS\nJZ4ApTBVokSJEiVKlCjxBCiFqRIlSpQoUaJEiSdAKUyVKFGiRIkSJUo8AUphqkSJEiVKlChR4glQ\nClMlSpQoUaJEiRJPgFKYKlGiRIkSJUqUeAKUwlSJEiVKlChRosQToBSmSpQoUaJEiRIlngClMFWi\nRIkSJUqUKPEEKIWpEiVKlChRokSJJ4D3ef7Yr3zzFWutBQBE0wiVSgUAECgLT5Fc53kelFL0WSlA\nGACAkBKeCgEASgYQkp55cHSMcRQBAI6PTpBl9Py5s2cR+PT52vUPMZom1AjloVZvAQCk8FG0J881\nkiQGAGhtoKEBAMYkQJ4BACZTKx7Vx2/95tdtZun5NE+hE/7CHUexAAAgAElEQVQcGyQJtcEPBMKg\nCgBYX9nAK194FQBwuLeHKJnQM2EF93dvAwAyk2EwoO9hNHSa80eLStWnsZIZXnv1GwCAs9uXIEQA\nALhy+SW8+OIVAMD/8U//CR7u0zub7Q56wwH1V1hEEbXt9//Pv/jMPv7Kr73m5tAY474XYvZnxhoY\nFtOlVIClZWYhkEl6Ls4yxBn1I9cG0tD3vhQIaGrhK4lQ0fc2z6BTaqM1OXL+aasNFE+L5wlYXi8W\nBlJI1zbl0TN/9mc3HjmHf+8//dtWGp63LIXOU2pnGkPntC60MdCaP2sNy2NhjIE2vKaMhTXFWAEa\n/IwVmB9Dg6JJ1n0S1kJIOfuWn+cB4O9n3xk7+61rD48f2cff//3fs7sfXQcAHDx8CJ4KSCWgAnpP\ne3UVFjQZ8WiKRrUGAMisxXQ6BUD7tdjHeZ7j+PAAAHC8tw/DLw1qVTRXuwCAZqcNzfup02yiVqW/\n3bn3AJK7011eQ5/n+s6NO9i5dR8AsLSxgS9+5TUAwD/+x//DI/v49//H/9lu+zR3F7p1VHnN7BwN\n8SCl748Ti6FP/YrqTUhNr9XCc3NUTSOEMe2VbqDR8mlMznU6aPP6/ODhEd5O6AcmtTaUpX1prYbl\nNe8rD77HR64QKLaMJ6SbOyGEW1d//j/9N5/Zx3/0T/43q3x6X7NRh+Xx7h0fwWqanzwdQPJ51KjW\n0GquAACm4xEmkyP+zREg6HyREICldaf8CoSkM9dAIZM0ThY1aBPMnuGz22AK49amhDa8kaFQVTTP\nwgYwoPb8r//tf/3IOfydf/jr9mc/uwEAWF05ixee+wIAIFQZUkvn/r29ewhCakPgBXjty38LABBN\nDX74w59S20yOo8NdAMDx4UNkvL7iOEK3uwQAaHVq6K52aBxUiGaLxmp5ZQONFo1zmsd4590PAQDX\nr16F4fNaIYSQ9Ixf93Hm4hYA4M9/77PPUwD4X/7pH9nizpNSurNUCAHhztFPs3sY9/+EmJ0rf12w\nBph/46lzi9fsP/oHv/zIPhpjrODTzcK693x6vxZp3FzLhHD/NDCf+Lixs98VQsxOXQto9yoBWww6\nLCTfJ5XiAvkMlJapEiVKlChRokSJJ8DnapnypHVaV60WoBKS9uYjQ8janu97UKy9+Z7nJHNAQEl6\nXikfWU6i5HgwQG8wpPcLYPvMBgBgY60NYcnSFJ9dx73dQwDAKM4wiQorz5w1RRsngXueB8HWDgPh\nrCCLoBLUkIxIi83SHLUqaXbWZMg1acPGaMTx7PPVq+9w+wW0Zu1ysINRNKKe+wb1OlmyTG4xTuj7\n6SRxv9uoC0yn1K8kjSBA75lOx2g0SKOcRofondwCAERJHZp7LIM20ixdqH/z1ij7MUlfijnZfE5p\ncNqHkLCW5rni+7CGNEudxU5LFxLA/OfiHULAOkuNhLS6aBFkoclBzqw1dvYefttC/QOAtXYTSUxt\ny5SPJKV1l0kfeWGNMho5L4wsz9y8Idcw/FlJ7drmSYkkI408y/KZBimla5mxACv58P0QEa8RbfNZ\n4yw+UfuU1pyyVD0Ku1ffw3vf/VN6/yRCf0j99QMfktdsbfssnv0iWU2brRZy1uaTOHXrIAgCeGp2\njLRZy68FISRbUGOdQ7MFJWjU4PGYmDjF3auk5U+OT+CzVSM5PsDGlcsAgMtPn8VqvQ4AOOyN8OHV\n9xfuY3i8i6BFA1rZ7mLFb9P3ykN+7yP6rYcPgYAs1eHyEho1+i2tQsQTthKf7MEb0Plx5cpz2N4m\nq0O700WeUx/rvRH8Ke37sBICgiwx1qTI2MqZGQPPL6zuHqwtrJnWWeMFxPyx9JkQmGnjVluoQvPP\nplCS5qpaMdheWwUABFJC8POjwwPI7ITa4o8gxJj6KpWzmmszhufTWsg1MNFsbRNNSNugNug6hCQr\nlWclckPzrOHDsFUTUrn1YrWGUIvvxfW1NWh9DQAQjSY42dkDANS8FJvn6KyP213w1QAhBd69+gYA\n4P69A5wcUb/W1jYgJe3dTruNiL0ZSZIgijL+2zG4u1heWcfZM2sAgHqziemE5vb44AD9I1oL8WQK\npBmPuYbyfH6+gkphXl8QbkRO7W3rzlExf37NPTN/zInii79GWMCtmblfoXNt8WnE3v4eJnzHVOs1\nLNWaAIBatTrrjxCP80pAiE/8p8Cnjb2BKSxiQkIUVjY7G2eLmcV//vMi+FyFqTBU8Dx21SnPuStC\n5aFaoe9933cLR6rZbWoMIAU1dzyeQvBnZS0mfVro9XqIs1tkmm00qsimtHnObGzAsNvr5r1d5Cl9\nb612E2mtnS0OKyC5bdVKgOxTzIafBIUKpCAhrhIIxDEJetNp4g7PRrMKBTqwAt/HiN1tWRzD44Pp\n5Ze/hIgvr3fe/4kTQvNMO/OqEAK+T2PUbFeQsWn/pHcIYWk8x5MhvvNHfwgAuHNnF/0+X/STKWot\nOhDTyRAn/f5C/aM+8KH98Ut95q2aLUgLZ/+UwsNKYx0A8NzzV3D1+lUAwIPdB4jyCfdpblMIC8ES\nlZQS4AvHwsAa6ocVFqIQ8IwF1MxEPt++xzF/VyohZOGC8RIoj/42TS1YHkKe5M4ELKSF5vdraSCL\nz2bmynn22ctIRjTGh8fHzu2cpIkTyqzROHOeLogvvPgabny0DwC4eu1NaO7v/MafP0rokFi8j9Fw\ngHRC77y4soqNOrUtNxZLa9SGMY7w4Q/+AgBw7rnXgQoLkplF6JOwoIRCnNLFNI2mKGRcoQBRo2e8\n2CBjBSPFPo5HdKhef+sdXFqmZ7743FlM+WLaORnDFzQ+XiNAuEWX2vlnnsMJK06LQMVDNDZJ8GnU\nam4PZXkEPSJB4tnVGhS7GkVD4eUr5/mPLd5+600AwKDfg9+li9JMD2FTcllOxiE7rIBJnkHz/vaE\nQmqL10hUFe1p+MKtK1gNxcqBNhZZIYArBaEWu4iFFLBF5IGxkHxO1asST50/Q/0ONXRCY79z+zYm\nE3b/RRMEilofqBSex22Hgcf9EEoi9KkjiUngSf7eiyD5+zhLoFnIyrMaPMvCqBCwHvUjNxZS0G9p\nSMAuLmhkOndnXCNQWKvTudatVNHku8TWu7i3Sy68YTRAPyZhJ00FgoCeyfIBEh4HJT30euTirNaq\naDVJyA5CA80XfppMsb+3Q+M5GcJjQanXGyDldZqlOdIJPV8Lq6g16bcqNR9pOl24j8Jadw9JIeYE\nJ+MufJif0w75+XkX8cf1x08QTexp8WA+PMPdhafahtOCRiEUf9z/9wh858//CHfu0PivbW5jdYXu\n6XaricBnl7GUToEWAggD+l5ICcEHSxaN4fP3Qa0JwRuKrgf6rISELFzrUjmFVkrpAimkJ9BdovvP\nCuXG3BjA6QDAY63V0s1XokSJEiVKlCjxBPhcLVPNeogwZG3CkosLoEBzydqYOSVNC0gO6pNCIghI\ng9SjBMM+aajnz5zFBpuxo2iEZZY2a9UAKYuKnqexf0yaQppYTGPSLBQywFlQrJNmtdbOFbG8sYqK\nv7h0Wm2EGLG6qoxCFJHGmSUxJAdiy4qPepM0uLWVDaTcniRKsbS0DABYX19Do00a03HvGDc+JHdI\nnEQAB5TWGxLLK6QxPXN5G2lM2rw2MXy2cP3svR9jd/cBAGAwiBBPqS8qMPA8tu74wlnNFoGdt+Yx\nhBCfqqkUM9pptdEOSCORiYdOQMGeF1+7iD/+3nfoeykhxJyWNqdVFBq7NrnT6q2yzsL5cd/ef6xl\nyjdTp0kLE8NosrxYo2E4eFqbHMIUzxjYIjA9y1wLJASihDRX1WzjpSsvAwB2P/wpxuMe/a0fQLNf\nJ8sybJ65CABY376CeuspAMCDh7cxGVNgtycVNO8bpRSUKKyEGuYxdCOvtYTDCY1JNtnDi+fI2uL7\nEg/u3gEAvPzlTbQSGvOrH9zAla+9AAAI2jPrmKck6j6Z7JvNOvKI+juNhuj12e08MDDsgn549SZu\nfnQXAPDMU238za+ROy8QGe4dFa7PEFnGQfw2Qp4Vc1dDZ21p4T7aIERnjSw0585dxNE+uYjGu5nz\nITfbNXQ7dGa0Wkt4+akL1P7pAIdNsh77nQ7GMVmbU2TIePw311bQbNMafu/uXUhONvG0Rj7zO0Hy\nehZyZkUw1sAUyRLzuQXAwu4TKQW0pnfkWQJP0n5eW65hqUFnzfBkF3duvAcAmAz7EJLmx/d9NOus\nmRuJkIPwpc3g8d7KshTRkCx4QeChUeGDTRpnJRaVDqRHz+/vHUBKcplOUwEBGoPUpBAhnXdSLCP/\nVDfMJ8D3sL5JZ0YttQjZ+p6dZLhxjxITDuMMqtrkvviI2OKa5UDC86akQsCut/FwiFabg+mtRJLQ\nOyvVmgu+j6cxHjyg9zeaLWxsk0VdBRZ+lefTN6g1aI2srHdRGCATPUGaLe6wkphZFSUEhCjOs5n1\nR8wcAvTv+Rc4N5k9tZY+Lbj79LfzZ+THPwCYy7kSc2EdsJhZzRbAT99+C+3OLwAA+sMAR4M9fukO\nFCeTYc66Tpb44mwzztVosxyKLZ5WzSxZ1s4F4uuK82goqeHNeSsKt3zvaA+/+1/9PQDA8sYKnMlY\nzMIurIUL91kEn6sw5SsJvzBhCyDnuCepBMADKoQHxR1TSkBIWqye8l3W0OZmiOGQMpGC0MfTT18A\nAFibocLuQmEVRikJI8PjI0xj2jyNxipqDc7AsDEEZi6zwiwqrIVkc2anFrrfXQTD8Qi9AV2UyDS5\nM7k9aUzvbG+tYmN9EwDw1MXL8BS17WBvDxfPU1/G4wiSHfhrq5vY3zkGAOTpITzOXHn+uTNoN+lz\nOhkijujgyGQFVy4/AwC4dfsG9g/oIk6jBFN2L3mZgeANrP1ZnMSjYKyZCVMfD0xyPmtx2sLMO2F1\nrYukT4LJres/xbe//W0AwMXLz+P7P/wr6p9NITitS8LO1rjAzBQurVvkFtIdB5RJVPxLuIPAWEDo\nxTd+3UtcnIuQOV0eYHceb8wspyMQAAwseH/DGImcLzhhgSihzfvDn/4Um+doTtob54EDutTa7Tb8\nIo4mbCIXJCzEGdDiDKIvvfIcPrzK8WV5jCUWELYuvYjeAbkrHt5612XJLYKlrU1sXX4WAHDvrTfR\nG1M723XKiAKAqtfGVp36+wd/8hO31l556Rz2d8kF+eDgGDbnuDAVIOT4ps5aC20+3N758DqGRw8B\nABVp8NQGXXwvP78OAc4ozUPcvEXr9ES0IU9oj25vrkKG9P4JMty+dn3hPo6tj9GYxrnfH+P2Xboc\n+70+BiOKpbl0bgsvf4kyBIXyETRIwDgaDhE26BJf9RrYfe9nAIDhSYRajQSM5bUxNF98ejxAh12x\n/eEAKbtEjRJQTvA8fdwWioJSswyu+ZjER8FYA8OuyzSeIqywYBUP8d67HwAARr0d+Bw/Wa0oF6+0\nutqFX2Tq5VVUK9S2KBq7HSWlB82hFs16iFqFxszzBKYxCSDaGPigM2WzCww5zmzQ68PjmNhKKJBm\n5Kr1/Dq08Bfu4zQe48ozFwAA9Ugj36Vz8OGDPewPaW6v3t/DyhbtiWeuXEGTFbad3q6Lj/UU4HuF\nMDV2F6/RCkFA7RHWYDqm56fRCCvrKzzOUwxG9LtWZOh2aY0cNgPUfFrvZy+uI9V0/kbTCXS++Hkj\nxalTbM7NNxd6Mhf+UPwveny2Xgw+puB+yu+JWRyFE+5P4ZQwdVpAO92Exft4fHyEvd67AIAstbjy\n/EsAgGqtBlsohPP3iQxcDO78916g3CPGpqdiT4v3GDmLH820B5HPR5XROj/qxdjlM+xHP/sB6k06\nky4/+wK2N7b5/faUMPkolG6+EiVKlChRokSJJ8DnapmqV6rwOKvHaAM/YM0sUPA8DlxMBSxnL1VD\nD1LR974XoMIWGb8ZImcej2s372NljVwUayuriCOS1E+OI9zaJQvRbm+CRLP7bLWGjVXSqupePmfl\ntHPWUgMdkRaWaYtIL64t5rHBpE/vt7lGvUVWp3Z7GRGb2F//2jfR50Da44MB8oR+eHlpA9aQljTo\nTzDhbK4sTbG0RO6E0WgIiMJ1mEBzMF6uM1Q8si7V6l0oj6xpaWbBHihM4wyaCaA84SGK6T3Hh32s\nrq8u1D87Z5miL4rvT4UtzgWRC/fQNB7CY419o6Vx6SnSjHXeR7dG7oHD6dHMtWftjGcMs89ksSw0\nSzjrAMxcdpOZef+IX2TxOTwaAFlaBCtrsFcHWlsYdskFnuc03Vxrp53HqUTOXEWws4zEjw6O8OMf\n/CUA4LUrF9DorvO4ATHzj4WegN+gMZGYIueJW1tfw85t0pxaq2dw5YWv0phoi6rHWvLeLtL+g4X7\nCGR47lXi7Hlw5wYeTmg9NtfWYSMOtj2OsX2W9utXnlrF/vuUVfXmRzcRcNB2LizWzpCVVSiFfbb+\njB7kqLOVqq2HeP4Z0vJ/4dVziNiCs3/Sx2hC/b3zcITdiAZr+cIKhuwWDPaApTbtb1GtwfcWt2qE\n1Ro4Thp3r1/H/oPbAIDpyQHAAcKXnruCiy+RZarWbGO4f4/GobuOX/0N0p5PTvbxzvuULPFwZx9b\nZ8g1eXxyjBU+w85urmCakWXtaNxHXCS5eMolzkjpO61aKeXCCjxPneITWpR7xxoDw2MZpQOInKyX\n/eEOdEYWv1pFosLhEVmaoFKhfdZqLUNxu+q1EEJQe1v5knN7pckYoUfW/VazgiiisbGWsrEBYDyO\nMRly1nFQg+XN0m0rZJxUsrrWxpCt8sM0Rq4aC/UPAJL+AOc2yVJwYXUJAz6L9+7cwcN9shBOohx6\nj8a+UWth+SxZwYbVibMWjac9hMxTqFON/ojuBk9VcfYcPa+zGBlzhckgg2b3vraAH9D+qzfbWF+h\nu0TlOTxBZ0BjqYrWEidujGJc++DGwn0UAvPpfHP/+AzLT3HEfOyR2TksPtHKaczMHSalcuf0p4VE\n2MewzHwWgiDEh9dvAgAO7u3jPFt/ar5Cxu2kAHo+1w2cK1lKBef+E6mzWEmBWTIWsVfRZ6UpuQyc\ntPYJd9T5py/gx1cpweQv3/iOS9j6xte+hd/6jd8GADSbTcjC3hQ8WlT6XIWpShjA+UGFdC45GUjn\n6hpmMXoDEmS6y9vwiqw9KaF4EPMkx9bGOQDA/d0B3v2A4okuPxdg/5A2z8FhD40l2syb52p4eJcu\nrIrwsNIkwaRVNy6N3cyZOxUskFDbhtMYMp5LTX8EXv/aV3HwbykLxGQaSx3OFPEqEGbMv2Vwbuss\ntaHZhOcXAtQAm2fIXL2xtYrv/+i7AIC333mA8+cpy0ipAIcnlNb9cHcPJqXDsbNUQ6NJn+MkxZtv\nE1ndrVsfYjSmgxXGFusTWlpIdqdRdsRfs5FyztsmeJmdHEX41teJWFRkI3z3u5TmLr0WXrjyHADg\nhz/7EXIWTCiRYhY0UFw+UihIzrLQoqBIAMUaFOmucyRuNs85G2YxZKjAskvGQwLBsWWZ1hB6JtwV\n2XxxksDj8ct0DsMCa240mhX6vNYIUGdXSq3eQMLuBAELr8qp6NYiT8htZ/0K/AatndQAmmk1lpeX\nUWW36UnvECan9V6rVTAZLi5o+J5Ep0vCzi//+q/gxjtvAwBu9Y8xZWXgYHiE7WOm1UiBeMrkpdMU\nkt1km5c20Vyh/XTtzj0cn1D7l9s1tDp0AW2dWcb4mGIkesdHqLXpd/d3Rjgg7x/SRhfrl+mSatRC\n5Bn1yxM+IlY2xvEE0WO4wRJrcPMOCVDR4Q6qPmdhDQ6wvkoX6B/+6z/AD39MQuJv/tZvo8HxMMrz\ncfcOCQ8//uH3XZbtdDLByRFd3NM4wiSiS2EyiRDxGkuyFDkLEokVsMWloPwZIbE3o4DxcjXn8lPu\nmUfBWotc017J0zEEC1CejlAJmMSyIimMAkCoaghYaY0igRZnn3mBh2lEwrQQIXQRZhGGqFTZ/ZdG\n0CyMhEGInANSg6pAlNJcjYYZ0pRDE5Y3EYRFrNgEqknrKJ9IpI/BNfPR2zfw/MoF+i1Rw/vXKd7u\n1kEP08IN5IUYs5t6f+cQFkUsmobHLsXhdIwpk+96ViLk8Wm1mqhWabwPDk/gM33GhQsXUWsW7nfl\nMr+ksgg5QzC4fA4Vv1AqdBFuhdWVFRcPvAg+Lkw5xU/MUyLMwhYw009dmErx9cyFN/t/FrPnYY1z\n7c1nsAsh3RFp598zR7Dp/sb9d/F5rFZrCBSdK4EXwuPYaUgFPKRDwD+zDcPKicgtfMzO0SJrj6I9\nCsbmWUwtKfncFy2dUEZroWj/7I5Tvocf8P1a8UNEMa2Tf/8fvoenL1E4xtdfe30mrS4gTJVuvhIl\nSpQoUaJEiSfA58szFYSONMtaC58tMkI4eiBIBeztE0/I5tYW6s2Zy0dxQPbRMEV8QqblZmcdb7xD\nwZZHUwWvts0vauA/+fYL7rf+7R4RYy5Xl7Da5iB1MUE+Z8UstAkJAcmB71UroMXigb3Vqu+E2CTP\nnUtuNDpBwpld7777Js5uXQAA/P3f+R38wquU5fDTt95Go0XWJRVasAKEZivEmbPUr1/+pV/Bn//7\n/xcAcPOjN5GzdWmcJsiYb+ve/R0cncTcRw3LbSDCQOpjnEwRVEjzCkOJx9Ey5vFp9p5ZSQS4MkDd\nzjaiCWvJfgNhjbTVM5vnEHDwbJqO8NYHP+V3z2WSwM5cIVY4/n8hxIzwUAjHgwLMFpWdcxcuglqt\n4YK5rfFhHSFnBsk8NEZnztysVA4U5VjmyoRAWoBdfo2qj06HLDiZrGGFCQGPTiYYJ+RO6HgphifE\nl1Orh2CPL0aDHu73aL3XjgbY7tLcLteX4GX0/dnzlzDsnyzex0YDgq1jjVYTL3yZXF3T/gAPbhKx\n692bN7F7jd4pco3znDW70VTY2KB1Wu82kYDd2tEEW+epX6srLbfXm2ELnTZp9ruHPaQDavOg2oLl\n7K/60jI0Z4WZzMBjd+re3j4M7/uljTNYW19fuI/7wxF2HpLbMTt8gPUWWRG+/gsv4NVXvggA+N73\nfgw/I+uOSGJ0zlM2Ze/oCN/77h8AAL7zr/8NAuY6qoc+dnfIOpIJD7tM4Kj9Oh5yCZmjzCIpsuyk\ncv0KvNydecbmLjBWCA9SzMqJeN6Cx7JOgZjcVSoZolpzvkIE7A4NfR+1GnMtZRksW021tcg4eD2K\nLAyXh/H9EHV24WqTosJu58l44MIIksTi4JB4yaqhhyDkcjzHCdKU+nRweOgCtbWZQBsaYxUuwcrF\nr53pJEWtRRbLo2mGeyNaa92nz2N7iaxIP/nRTfQeUHt0MoGvaZ9trqzCDGgeRsMJ8rQoX5WjUuEk\ni0oddS4vtqQ1JlN6Znm1je5ykfGnETOBcpxNHMdaEFSROWtkguGIntnc2MaFCxsL91HIOSuStTOr\nkJXOMiXmI9Dn4tJ/zhryqVHn/B8hZhyAOodhd5jnBS4DTswxJ1trXBKQ/blwicWt/fcfPIDW7E2w\nuSNgNkIgu0MeiuR4D8svfok+BwFStsTlc+THSs8yz6WUzpX5ad22c+mxYr7kjDW4eJ4ScKqVOobM\nfffs5WfRbtJ6qwQK8jFEpM9XmKqEyLKi5p12kxcoDx7H/lRrQM4XZW8QY71LHfOkBxXS5jm4cYA3\nuOaSalTQ51TY0c4RNpmsrhou4ac/JldEnvfhsXC0ulxHs8GbKlMQnMkRhuGMpdcYRzOQ5YYYwRZE\nEt3FF1+iNty4/gDxmF17FvCZ/HH/aAf9KZnV//DPOnj+BXJxnTmzgb1Durzef+c9vPcuuV7u3LuJ\ngFOXv/jyL2Dcp3def/++Y0ZfW6/Ck0wW6tWxucIHXxqjUaOYk+kkQodTgnMdI9Zcf86Mfs73/mmg\nNT0npDDm2dALLzZAC97nQ/ipCy9gZZnm8MzZNWRFuvlogm6Fnnn9pS+id0xm33tHD4AiRkbgNAXC\nXP0q6ZjRZ4eLtfPZUh4gFje7d9fOIo5J0NBZjJQPUk/nyJmgMk2mgKP28GF0cRkaeEVqnwZyFl4n\nY4PxkC4+aSOEguZBxzGyCWUKeStVrJ+ntbB/7wZEhdqQpjkMExRuX3wGOiBXzfV3foycMw0H4wnS\npLdwH3WuXZ3BKIpgCibvpQ4uvUSxQsrzcHyPXF1mNMZKk2k4ztSwtEJtyIXFXo8EipfPdrFxjmIZ\nVegh4VpxJwfHyDhesFJvYY8znfytZeQsMdrcQDGtwtjmaDDhZ2e5g4inrlILHV3IIpgOetj7iFx4\nG40QX/o6ZY/+3f/it7C+RkLZ5S99A+02ZVDeuvcQ/+p//z0AwIUzW/jGL/1NAECrUUcypTXQ65/g\n3/7JvwMAHE8jrPBlunyug5SpTzIlgZyJCDVcvbo0TZEx66uUgmNBqEaozxmUUklHLvooZNEIhuvr\n1WSGfOL82sQsDcoinid1LMg2Uz0FJLO0I0S1Qi5ZJQQ8Fv7SPIXyOZu64gMsdB4e9RBnJLAoT6PK\n51quR+53m80AUUJnXG6mjtzS1AcIls4v1D8AsFUfb35A1A6VoIK1554GAFy4tIphRGfltZsKU9pC\nWFup4RU+T/1OE/YWta1ereLOHYopNNKgWqF1d3hwjKeeJkX1/NOb2D3klH05RlRQzeQ5hkM6cyfT\nCRpNEhK7KyFGUY/7rpEmRTbfGG2OcV0EYnaccaf5ezH3D3y6wLAQnFtwznUohavoEE0nqPAZTPGo\nc0Kc+1tx6j2Pg2ajieMeZ5J7PmI++6tJAo4ewNFf/jEOb9J+tecvQnGYg5CBq19aXVpySqlSM8Hz\ns66v2T01i4s22uLSRTK2TEYpJGh/r3U30e1QfKcnhRPiFkHp5itRokSJEiVKlHgCfK6WqSAIYS1L\n+5pKqQBAqHwnPdaqFTRZUzw4nuDpC5zNF9Yx4Ey9qQlxzJ+roUS9Q+6HcawxYleXbDbxwQdsWg6m\nUJz1Mhj1sbXGgZS+gin4h6xG6BfD4bmsIWMBydwpi6Aq+48AACAASURBVGA42sMym7enm2exv08S\nr1Qe8oDavLa2Ca/K5W1uv49/9f/8CwBAxV/C7Xvk5mk0GthYJtLGYX+KYzarf/ev/gqtGknm59af\nRsqWkjMr62hW6XeV10aNtac8y5El9Fv7hwdYYdN1ECr0IzKZX7v1M8Txoq7M0xxSYs6EOg8X/G0N\nfDaLT0YWnfMU3KdUG4mhuZr27+DBDmV6NJsBvv3aVwAA//JP9uZK+Qg4V6SAc7HN1zGTQjjSVyHg\nAtY/3uZHYevS85hy+ZN4MsC4R3wkaRK5sRSQ0Bz8bQBotrAoYwAOeqXyBTSusZHw2R2ypEIc3aUk\ngqVOC9ubpG1naQST0VpbWt/CcEr9PT44hGTLha+kcwX2oxMUxcSO9ndhF7RoAMB0OnH1BKVSALul\nkjR2vFfPXnkWH3D5kQ/v7aLOmmsrbuPufXYR1qvo7ZIFYm1JQnIWbDRJ4DFRY7dRxz5bU/cORrhP\nwwatAxjOemtYiaVVWtedrXXU2TUVJTFq7AKLTYZ+b3FXpk1ivMAcdL/9G7+Kv/N3/zNqz/IGxiNq\nv6wpoMKWvtu38X/93/8MACWSvPQFyna8+NIXscxB8560EJzA8gd/9GcYD2l8aoMp8oCJLxtNePxO\nmQt4nDyQ6Qxpyp+z2VxJkSGRtE5831uY107rBIpJrIzREEUJrGoF4ykTUdY6jji2VgtdemmeZDMO\nPDkrvZSbFMYWlqkMyYDalWuNUY/2hB80sLZJlj2BKUZc73NtfQuVClnBz599CnFCc/5w7y6GluZt\nahMou/h5urrVxWhCZ18U+9jcIiuS8BUaIe2JeqPijoZWs4bVNXLb7Q+GyHkcXnrmOTCnKTIvw/om\n3TFX3/sAS11+TwtoGw4oNzGmbE0b9afoHdOitVCO1LQvx0g4g9L3feQZNeLg4BABe1oWgfjY59mp\nZWc8iJCngs2dhWhxT9vP/ZiAcH+fJokrpeN5CtUqrWUhFPK5gPWZpUY8hpOPEi4s18r1lEKP93Fn\nacmRNHt+ALVK7lHZaEGEheXUQsi5UI5PII3+LHySZcpagTtMTpxFmSMB31pfhbS8RycThFU6y597\n+qlH93Gh1vw1wfMU8rxIfZw7+C2cC8cLfDRbZN67dn0P7zfI5bO2sYXelM2o8FFbJgHKSIsKHwph\nFagyUZxnU4CzSYynUfAD9EZjpBlNUq3uwSiObchztxmEVLDM0hsEPnS+eJZUb5AgYKHMD9rwVGHa\nDODz5b61vo3NTXJf3t35KfpD6mMyHWI0Jnu1NnU0mYH5K699Bfd3yEQdVFJc3qKDTOfnMTghM3On\nEUCyYBjHYzRafJEtdXF8yG4qBUQTLpI8TXF7l373YPcQzcai7pNZvTxgljorxCmquTnfuoLPGUQ3\nb9xFOuVYJN+gwVk0m80KdpiM0eYTvPw1Ygp//uLTeOcm+dOt8mbngBGOoE3IuTgpgZmt1dgZ35qc\nURQsgq2nLmHM1BXRYIAqF+Uc9vYxHfNlbjTSmNN1rYXl+mRKpu7wgQA0U1QsbXbR3iChKVh9Hqsr\nJFQiAFob5PYwkz56e8x0349wtENxfqvdAJFP9AMmTdDgTJjzF6/g7l1yw0Enzm20CAbDMWxB+WGt\nq0DgS+WoSazU7sj0gyp6MQ3o968fYv+AXHvdZg01VpBeeTpElwsmj+IxMo6larS7qDTogtu7c4yB\npTE51+ni6LCIOZJAZXYBCTE7AAvBJ2i1Hot8tV2r4GtMTPrU+QtIWIDRWqFao/We5wIPdkg4VVLh\n7/zK36bnL13C7QdMNFqr4/wmZd92WwF+9dd+HQCQaYU3f0Tp1To1iJisNzYSktm2PeE7okxPKZcl\nKsXMzScgkbFLdDpJkLC76FEwOoFkN7JUFjW+ANutANNpcbQL1Ou0foWEiy/UmXF7V2uDPKN5C0Pr\n6p6meYaUxyzTFuCM663tS05BWlmuotdmmoRRgkaDLsNmcxXnOzTnRngoKhHnY4s4PlqofwCwsdpB\nyMWTq2Edu8x6fuujCWpLtBaiKAc4Xg1WYjCgPTrsD3B2k9qz1mjiwgadubaVo9Wp8BhuIkpIWZru\nAUnGbucsR8Z0DtNhhju3aJ0G1Qaspf4mmYYGjZtSMSJWPOr1Gg73DxbuozCn3VWnhaniszktOM1l\n7T0OTj1uLXy+q1qNGkIOeTk62EONsx2FDN05oXMNWdTLw+OJU8cHx8gzeuf5809jc532U+AFgGKj\nyrd/DZJjFjWsy+DL9azihafUjOlczOgfpPR+TqGnLmonTM3XfZXSosWZ9p1zTbz7AbkXX//Gl1Bl\nxTWPU2Rm8Uz+0s1XokSJEiVKlCjxBPhcLVPK5vALEkal4UkmthMBFJctEJ5Fm60qkzjCD96h4MO1\nwymqLbJGPdzdd1kxVkxQZ019rbuNLCGN42SQIRHshkmPsMQutnNnzqLJVcKFSCGLwGRjkXBwaLXi\nw2dpWUDMiLsWgI8aFGsujWqAjQ1qj5TCaXNBMMZoQKZErYGDPgfjhRsIC/eAktCmKA8h0GqTlSrT\nEQ6OqM2dpTOQzMnk+REkW/sqvg9ryLIy6E2Z3wu4cG4Dgq0Ow/EBlrukaVYq2/DVgkSBdt7q9DFN\nwGWbzLg9BEIEXLvNqzbR40DOag0IWUvOhI9zL5BL5d61twB2/Xzp8kt4uE+a6NF0BDFXjsPpRFI4\nDdtI68jarJxzNQrAysVVuKW1rnOTjqpV+GwxsbP4S2RJAsUB/MoY5Jly41OUw7FGgjkA4YUehKE5\n3z+6izqv5VCEGOxSdlja38N4QBrtaDBEh6YHldYyVJv6vrq2hRq7oI/NHg57NM/DOENuF1+ng+HU\nZenUKyFlHoKsFEXtK+WnaNX5d7sdHLCrTgiDJXYpS6NQ56QGEwYYJ0WwtY9BxC4iL0dnnawCa9sx\nRJ++X+s0XEap0BYVtuJ58OCz+8oTCpKtDtPxGBG71RZBRVl0W0XNwZqr8ymUgMefl7pLrjTKyuoq\nXv/G69TmPHduj6fOPefq2A36Jxhx1pYUBhfOksUw0gK3H5CFS2YpAg7KhsxnJTuMdbUUPTVn7RaA\n4KBv/zHUWylyJAlZ7ZqtCprM5dTu1FH4vZQnsLxCAbXD4cBZpixiZ92VSsLKWWC65D0klILk+npS\nWGycIQvq2uq2CwKGjbC7R9bRTAMhk+++/e5NR2pbqwq0V8iaLitT3Lq3v3Af0yRCs03tF1LiDvOG\n9Qcj1Fc4SaG7CrtFc7W1vol2g+b8lS991bmy3/vB9zG9RVZfURWImM/IALh9mxOVMoEqJ8uEvoca\nu7Vfeeoymlzm6Wc3b+Lau7TnNrbW0WzxGRDmztKx1O1gzEkri8Aa686M024+oDhxKCN5LrvZWVue\nzB5S1PmshAEs83DdvnENr7xMSSheLXRemtDzkHGiyuN6F6t+iLrirPLRMQyHmOhaiODZ5wEAyfqm\nOyN9beFzCIPWZi5kw7oEDWOM44ecp2Y7nRg1+37eG2aMxcoKrZObNz9AziS+S806qiwfWAD+/19r\n81VDBY9N21p7bgCs0i4DSlsDw1QEmYgwTelQ8KMUCbvk0kxhxGngX3ltG2tMqLa2tIUoos9vvHnb\nFRlWeYrLT9HAXbnUdeSfQOBiGBTIXQMAQs0RigoFYRcfpmbQglKF20mjzi4i634FyLIRYKg9Wxu/\niBh0sQ4iD5YJ8AKbImDGd20kwNl8eTZ2MTlPP/sKjg9o4u8/eAuGKRCEUKgyY+/+yRBLfCsLKzBb\nkxkadRISllpbCzMjzJtWyWw65+YrPhNjHPVYztxzSkkorgYaeJ4jTM1yidUuz8+LL8LjjKNM5/jy\ni68CAP7iJ28g4/nRYpZlIeYZ7+bI2iDmqBSE/EQT8Kch8BQMxw35FR8Vjt8JwgrALhAI4TZynuWn\ndm3KZvGjYYYDZode21K4sMbulmyIbETzHFY9Vwkgy2MEbMtf6dQg2H0SZRpt5snorp9DEpFrYTC4\njdsPSPiKotnluAjCsOIEaE8JGF1kmRlUmWg0GyeQpsj4zJDzQbfVrGHK8VyTJEOe0phMIo3+uKDk\nkDhmOoed4z42YxI6lDHuUJ0c7qHL5JlRnLlsKDRDhCErD7l0BWqNTRE8Rlr9C08/g9UNLlBbq8IW\nnCVzw2RhsbGxwWMSOLejUtIVZa/Va+gdk/u9f3yAHscO9QZD7B2Ty2ppbROtNgkSt/sxas3Zb2VF\nCINSkCwtzWfskYuc98JjkJLC5EDB9CwFAlaarAWaTLFirYZheo44ztBZImVNyCmtZ1ANzIKOIctT\n5xM3VmA8obW2uXkObWb4braXnRv27r0dJ0AFfojjYxqbKDJ44+0fAwC+/a1XsLJMz4QVhYcPB4v3\n0RO4v/eQuysdAery6goaHP+5tbGC+gqtr28+/2XUm9wv3+KYmc5XVhqwFyjT9PBgHweHrNS1l/Di\n2SsAgN7xADu3SHm7/IXn8fwLdMmfv3gOX2X3Vvwv/iXe+AmFHuzoI5y7yDGoNsXxUZHZl6PGhKgL\ngTTUT/ofcxnMcz6qU3FPH/+7TzoD7Mf+fxGGIBFyvxSsI9l9/auvYX2VBNjbez0IFvyDwKf1UbT5\nMXyMOs9xTrA7+ME99LcpbKW53EbaIUHVptksXmmO3R9CQugigxxzrj05x/I+q8yhlJq7l2aZiQBc\nNu3RwT6qkoTiaZri4pkLAIDRIELIsY/9yRQtpnTxFxCVSjdfiRIlSpQoUaLEE+BztUwRt0oRgOlB\neSxteikA0ibiqe+07eNJD55Y4b+VyHJyaZ05s4W0w3+bD3D+LGmxvtxzNZS+8OIafnaNJNulynl8\n4VkKeGtWDFIO9rTCc1YqY4wjusvzDDmTNipPIXwMU6qINfKQ/lYbA82ahRFkNgeATucCmi0yo8bx\nMuKCg0X1UWtzdlBdIk3JhO97HlRQ+KwMLGcHPTw5xMbqF/n9Vdy79xMAgOdJpFnBz1RHnBaBrhlC\nn/mz0ikM9zEzCnrBZL55Xid65ydZpqTTXIUUjuPJYsYtluXGZcVEUY4jDqTvPbiNVoMtZstdvHDp\nRQDA3lHPBaMbX56yiLkwTSsgTMHdY51rTwiFxzDaYO/BXQzZ+jDpn2B0QtaK/sEOhj3ioYkmfcSc\nKRQnMSr+rF7UiMkwkySDYMtOteqjzhw88D14bEWoeEDAVtBaZxNTDrae9PaQTAr3g8HwiDS5w0oN\nS5ukSY8HMQJOUQobIfLHsGrUajVnmZIwSOMiESNyayGfJkj/v/berMmS5DoT+9w91rvmnpW1Zm3d\nXb2gQQIghxiSw+GMTKRBD5oxyrSYjPMDpF+g/6EHmc2DTDLTg140NhppJIEixQEBggABNHuvfct9\nuTfvGqu76+Gc8LgFgNO3psz6Kb4HdCIt697wCA/3498533fYBGaWFMj47KWiFgKmMktVQFW+RFmK\nwlYKHIVZRs99Op8jOiOWar3Xxhq3KBmdn2LK11yoACm3GfFji6hF45pONOZzVqApoNtf3r/n6s4V\nSBakaFH7oBljYFX9c8VOB0GAq1dJLVaVEQDAeDzG+Rkx4Ud7h3jxktiL0SzBEc9b7cdOAdUzoWPa\nledDVWkz1O+I8lRNRiywEq/DoEpRMxdBGDjmwliLdovTkqMLnLB3Xa+3gooQk8qD5LS5r4RrjZTk\nAj6zoLYwsJzmi9t9hDG3okkTJNMh378Sq2u0Rk+Hx44EuPv2Pdx96zYAoBPPMEm4gDsEonh5pVvY\nqdNMo0GClNNMoiyw3SFGI/YtPnyXxB2dEJie0/OZHpcYsEFsNwqxuUPXmZdTbHI7srff+QBG0fP/\n6Gcf4ZhTu60gwM2blNa8duOKe1/vXrmEH//Nx+76NLMnnlKYMyubzOa4duPy0mOEfbVlS53QMnjV\nbJjXswUjSvwH9M6rvsvzJdotWpPKdI519sYqp0N0+BnJBV9BpRQ3zXt9FEUBK1hFv3UFm5fp/kSB\ncm1+4lIjq0okbImgajmDml0yumZ1fd+vWSpVp/+Esr+UKl1Ij/I7EoWBK4JPdAu/4JZ0P3743+Mf\n/zaZaI8GB/iTP/kTAEB3CbPgrzWYssY6KwIpfdTEmMR0Spfy8WfH2DsgalkGK7AcCBR5Dnh003v9\nbXznd8kp9bNf/Ft88TFtsr/5rRtIE6LdJ6MpVtu0uF3f6GOFqWhphpBVutDCqZiskk4xIAVQcuAm\nhIIUy6v5CpFBhJf5327BcEpmls2wdWkXANDduIPxnCbxdDZHFNIG2urkaHFzWCkVZuzK6kO5WiAI\n39WXQRlonijbOx+i4H51w9OnSDk9aqxAxjVIyhPuZcihXTBbmimMWl5m+utqpuzfsyAIYZ3UXnp1\n3zFjcvC6iCTJEPCiUWiLR+zA/V7cRsqGrO/euoejc1qQjyfnr2wc9ZfWQRa99/9h1gihHzpl5HRw\nhjGbiCbjU5Qz2kTyNEHJJoZlkaOsHKwhHDOvIBDwnOp12wja3GtvPIXmZrIyXgG4WXGqAcNKktbO\nLcwefsH3ao6tK6RKO3r0JebcKzKZnmGbOwR4XoQ8X155kmU5rFfVQgjXz6wsSxi/qlHKMRtTZJUk\nGkO2z3h4MoDP6qxQCkhuyG1We0g5xVmUJVqs4CutQJ5zGtFq9FqVIshifEH3c64ClHyYCfyeM0ZU\ngUTBxqSzIkf5GsUa56ML6IIC0s3tLVi+P1maQsW143g1ZzzPh+IA3BjtgqzxeIyjIwqi9/f38eQp\n1e08ef4C4zm93+n+PvwrtEGvb+ygBL3fSim33+mFhunW1GkJC7g027KGnQAQ+gEM/zshFGSligoD\n1/cPQsLnRse9/hoKnrNpmtQO/p50JopB2MHKKtW3zWYFrOB1UPquNjHPUxRcY+J70pmSSmFc2unk\n9BgrPT4wrFhcjGiNm89maPWWD4h9T6G9RvMo8jLc/5waCCtfIuT0uM4nePmUrFWORhr9iE2fJwmO\nOO147949rPTpoL19aQvdiDZHT2vMp2S90IKB5Xl6dHiEU075bm72MT+igDQsM/i8uvW7PezeoM9Z\n6Un43L9xNJmgyynfZSCshVjsHeokZ1hQoonabHOhdtM4O0vwL4X7DNf416L+WUi3BhelwRnfH12k\n7nC1ubmNzU0qPYmPhij4M9utCGO2HhJisXb2q2EFEN/h4PTmXWcTU5Y5Mi5tKa1EVjhXTYhWZcZc\nd7nQVkMxIeN5HoqyKk+Qdf3Ygou8IGk5faS1MLqqKxY4PqOxPH60jx/+hFLSd7/9m/jX3/9Lup7k\nBHffpoPrrSWCqSbN16BBgwYNGjRo8Ab4WpmpLM+QcTqh024hDOmkMEwNPuYTx/2HRyjAarhoBzl7\n2KTZ3Jl4HR8fYXydKLr33/0u/voHROt+8tExDtkk8/GLDBtb5Fmx098EDFHCUijq8QAABWAWTotV\n22wJoKwqsi1R9ctiUuboamKm/P4/QMjMQRfaUeZHpyXg0bisJ2BZtVAYiyytTrQSgtNjuhDQaZUq\nq7MCvVhgcEaFsQ8fDXDnbVLEvX3vGwi4GP1g/wwvXtJJejqfwXCEn5ZrCHw6XQo5oz5fS4AYKPf/\nFvybxCsn7ep0Za1Bu0Nj6nc2UBT0+9m0hOFTcpaXmFg6YaxvbGFthdi5s8EFjo+Jubh2bRv3dsmb\nafjpBcqFospFPsylIBdOctYKvI673Sc//RHmXDg+GZ+jSIndyNMSCfdTK63nUkWeJ2FYJVmWKfrM\nvKx3W3UB+sYWoj6dbsLWpbqFTCuoLNBQmhKaizSV9BD3NvgzE+zco3Rn/9INHLwk9VQ6G0Og6k0l\n4S9vM4UkLWBZceZFEh6nC7VvnKTMqAAJnxRDT7nvOpil8Ll10eUwwDorUM/HOUYpnfbiuIUtTqu0\nV9qI2WupHXrO68ykOYRT/80RsC9RJwhc0WvplbAhM0rTDPY1fKYevdzDe5xuabfazqRSa+PSA55X\nm2QaY5Cyymg+n2I8prHsvXyGBw+J/X7w+afOY+vx833kzOisRh7a3JIlXruGjNeqQNfmsblaYKas\nrdtXWYtqFhvzKsP774OvFCSrDPOsgMfrlFI1A6w8H5opYM/zoXiSqKnv3httLUK+B0HYcf31VlY2\nIRQxb0VpkXM6ph0I2KB6tyTGI2J22kq4NkBxZwOdFn1DNn8EnVCxrwwlOitrS40PAN679RZE5TMV\ntHHrMq374/kEly+zuGc2wt/+3+T3VZ4VuLxOv9/bP3GCiChawc1bNBfCoOu8qB4/uO9UbOsb24g7\n7In39AWu3Sdj3W4rwMlz8iGSxRyrzJq2oza+9x/9xwCAd96+jGReGZya1xKDWKPr9UkoaFMxpQEE\nv5dFXtSCLWvrfnmigOD0uCc8gBnyEoAV1fwyztPMaIuLMT2LZy8OMZgU7vPbEd3n3/rgLsasmt3e\nXsPZmNaqLJ3A2ErlrmHMcn5oALFCIStrrZLIC2ajyhKC/R1zXcAUVU9Ui2HGrKiSLmNjpXClOXmR\nIM/qFjXVO1Qa5fYfUozz77VGUflNvnyCpMosmBytFo1rfPgC6ZDG3u+F+Ff/278CAPyzf/qHXznG\nrzWYAsgYDwCisIWCX9q//WwPnz5mB+xegC4HHXYeQLECxxiNgo3lBoMCP/jBDwEA3/mNW9i9RTnO\nTz76Wzx6TnS89nysKtoEz0cWaU6qoX6nBXCzz8AKFLZKjQjqsgzAKAnJG01RaMglbQMA4PbON7D/\ngCbrl/d/gSvvvw0A6HViGM0uuqKExwuEEgEm46rfm0XJcnJjSpRVLjn2nboQ1rpFcFTkOD4gmXHU\n7uLZHk36s3EXQVCpdm6gc4Uo206RIuHNwstOIHIKQm9cjlCWy70Yv7zQOzp1gZImoQfXuejCpUxb\n7dDV41jjI6tqxYx2dU9Rq421PqUZjJD48z8nynV/7wW+812qD7u+cxUPD58BoHRotUVJKSBRrTjS\n1RNYA5fGWgYXZ+euFiUKW1CVpLYoUFQWCEZC2MrOo+bd/ShAx68MPD3ILgUIW5eu1vVlYQifpdZC\nahRTNjFUkZuDtrRQ7LQdpFNcPCWLEK+/DS75QzY5d/fNWIvlE0TAeDQGOA3ejlouzSRFhB73v5sP\n5hhMWeKthEuDl3nm7CKEUAgq+Tw8nLKC72w4hsdB5aXNNZwd7tO9lXB1OxeDmbOUSAuDiE39rJa1\nHDv0scLBdZZYZ4y4DA4HI7x/mw5UELI6K1HqbaF+Si6k96E5bTc5x/k+vR/Pv/wEL7lnmEmnzpxz\ncHoGzanAu29dRcg2JYPzY/RuUiCp4UPy3JOmeCWAqs8k1ilDfzld/u+DEBIrPZpHushcmte0fARV\ngGgNFCu2/CBCyD3p8jRFEHLNlCdQNUD3/RDT6r20Eh1uApzl2pkRBz5gvCpF6GHC9VPSAzod+t7N\nnRuYTcgq4vnRKTodVrJ2+zg/WH49/Uff/l3X1Dz0PQgOFhJrICTNhdBqvPgFrfs/+uQjHIwppTgc\njJ16+EefPcD9F1R3uNPxce0abeyFnuLBF2RNcuWageCU6MXZGL/46DMaS78NwcFgK1RY5RTe2eAC\nIV/P1a1LSPlgYIWC8pavC7OLaj5h3VrlqcApRI9Pjhd6LNbNfrU1QGU2DQMVVFYW0s2HsiwRcu0g\njMBoSPfn7HiAkmuVpedDcJry/uNnaAX03tx4exdT7j94MUsgOJii8H/5g40FMB3R+2H0EF22P7LW\nYHhGNdJlnrn9pCzrkgUpJQbV/VlIy4sFmx6lVP0OhW2ssam38JRbt6rPAgBf5xixQtcI36WnZ6ML\nt+/6fhsPvvx86TE2ab4GDRo0aNCgQYM3wNfKTAkIV3iWFhY//Tmdtj96dIgbd4iN+IPfvYunT4he\n/+sfHcNnBcnaziY4w4LjlycYcE+6Tx6McesqqUaC1lX4MVHOmRlinFLkKYsxZjmliLZa69Aln3qt\nQppQtJwXeU2dQgCmUvZp5PnydObv/f4f4s8efR8A8IujZxCrbDy2vomA04uTyRHKjCLv/soV7B0Q\ng7a/N0SeVCacFt0eq7ziEIErLg1dkepoPEOLT46hamE0oBNHklqAFSrK82Es3atYSng+MXRB0Mat\nK6RcunurBbVkJnPx5CykcGZ/ZuFEZSGgUKkkcwxG3BJo4zpMxidaI+FJGp8KDNKc7sHpeI4gpOez\nsd7Dux8Qs/e//+t/i7M/o5Po7jvX0OWT9zzLUfl3LRpzUr3+oi/La3iiFJlj03SRoeR+c2U6R8le\nSDrPYVmpZ0uNiBVqrW4XAauh/LCNLvuDdYIQgz1iX/vbt2CZojO+hGJPJQ0fEbNaQguk/L0q7iC5\n4G729gxHe1Sgn8wv4HHbBwPr3o9lMBgMYLl4c3Otgz63cCoLz/kVXb55HV6bT+qjc/SYap8kGXI+\nOaZBiNMJPZe4DKGZrSutQcJzfDJPMEtoPoaBwpBTCPvHEwhJ98f3PMxGNN7JNMemR7+POy10mPma\nz0qMR8t7FF3kFgUvcWVpYat8qq0ZVUrBVSkrDVvSteWzC8yHdGJW2QxB5f8W+JgwuxPYEprn/52r\nm+h26f781ZP7iKp2V1vXwVoQ+EaiLBfFG9WCU6uVhBBLF6F70kObGYe5Maj0B8Z6yLLCfc0Km0nG\ncYyIPXTSVgKp6G/CKHBF5Ek2damQ2WQKj00sBSRm7KklejFUJVKYnSBStO4YrbDGnn++Z2FZOBD1\n++j0qLfZ8UBAF8sLJfpxi9JgfIdKvjehElAsuOlI4Le/RS2oPv+7F/jiJbGg1vqI+V28uJjilNfH\nYTvAlFmMokzxdEDP/HD2BKZSYQae6x/3859/gm/doxR9vxdie53u5/7xC1ywUjKdzWF4HfJ8D+Fr\nlIZogwXfPOuMpG2RIlTE6KEonBpcCOH8knxPwbIqI89ziIwZnMB3og/f98H2TUjTDJpLba5u7uCE\ni7C1tQgXfOfuvvMOAGB7bR2rPVoDfvDDnzlf8nZRywAAIABJREFUM231awkJrbVYWSOmXSnPtW4j\n1WwlltDwmEU1Iq/niahTdUIqd690WdbiMCGd8rHb7bm/Mca4dmMWcOX6vtK4mBHbGHW3sL15FQBQ\npinEBt/DXFVexkvh603zeQrnUxrYp4+e4rMH9HJ6wsMHNykXfvPyJj79mDbfsLWC7gq/nEGBI64V\nOT7ec5LOdK+HwQU94EDFWOM+Z3oInJ1QYBVvruLgjCjnG1d7CKuakDKA4MXIllMo1O6uhmtXAgVX\nK7AMji5GeHbOfd0yD/PH9L3p8QQ6p1qLNHmOnSvsvnrwOaZzoupN2nEOyFYXSOdcUzQTEKZunlul\nCtIiw/mAXubz0xCSF4i47aPDRp2RLDAeEY3t+YDHKpZu9wrKDaohGI6BqJUsPUaXhrB1nRSEfUVZ\nVwdcGpMJNzlNRlAlB5e5cinfbqcNwX2/oOc4PidaOfAFrl4mM77v/Sffw2DE9Rs6xVaXxrGfHMNF\nwVI41aMRC/37BFxKbhlMhifOOdnqwrnylvkcJW+qeZE5F2JY4QKouNNFxI23494Ghsf0/MvxCTx2\nDU8uDuDFtLD4QQTFvQvnk5EbimeBfErzaDY6RZaxUedFhpM9qoFjqQoNHWT1sSza3Y6z/7CmRMT1\ni9IXSPj3eaGxfomC75dPD7DGKaWt1VUcjOmZns6nSPl7I6udxH770ia6nC588fIlVtv0vq6v9jCb\n0bxOSo1pRmlB5UvEXE/38P5jxGv0b7ubqyg5MJ+MRwsKzSUQ92BklXL1nTGtkrWiiYyl2QzWaKS8\nSZXWOHuEtf4qbl+j9SmbjJBNapuHgjf69X6MFqf51mcnSJ9QX8XueguC7VrKQkCpepF3xrZCOaWc\ntdZd21fBGos+uzXPkimCsD4AzrlcoNNpo8/PYW1tA+NBZbeh0eZUrVK+U5ONRyfY2qKNZbW/zapr\nkv4bTmkaAdfnUMgYStLcjFp9FJrmwsVoBt/nGrjeNZxc0M0/PrUQrHRcboxAUdT1ZFX9qoBGMabr\nSc0Mb92ha17b6kPsUemDhV/XDQmDjO/x/niG04d16cElVmxtb2wgnVPA1e/FGHEw9eLwCJGk+7a+\n3UObLTB0keGCLVR63R5Svj8WgFTLz1NjFWylrLQGPgehPgw8TdepytQFj4BFxrWYhfCQc11YkeSI\nOZ2aW+t6PAZ+6JRuo4spTk+JZOh3e+jwSzEvcsR8gAl9gYsh7c0fnz7Db/2Db9IYWxGev6A9rJAG\nVi7ppwNS8XpcOhNHnjtcGVisbVziG2EADuhyXbpr1kVZvxPCOlWsEsodSqXvufpn6fvOgFsK+Wpj\nZN5zfOVh7wHVad95bxXv3iWD1pODAzx4Rund1dY6snT5w1uT5mvQoEGDBg0aNHgDfK3MVF6WePiE\nKNhP7h8jLbkAttXCx/eJUj8aXODojAspVYDhCac0Zmeuw/XVdYXhkE4f55NzjDw6QcRxD60OMQSe\nF6LgNEOSZDA81KOTAa5sUkpRgnxSAPJmqWrelBSQfEKVMFCvwfWZzMdTVnVMsxwhMyWD0Ql6bWIa\n3r/TcwV4n48fIJtQ9KtwA1WtnDHaFcpLz4NAxSLUEXCsUFOYqQW4TQeKCCeHdIIIiwNs9ojGvnpz\nFz/+yb+j62l9AwcHpzzGC/zxH91eeoyLqFkq+8rPhqlqBVJiAsBofILtPp0gjTbwuThUGw3B5neX\nt7bgcXF2lozQZpXFzd1d/MOrZMx3eHiM+1+yQaloYe+MmMzcFKjavVhhYVRdqChfg5NOp6PaANPk\njrEsiswZvmptoPjUKyFgmCGK17bR7XIvwihE0qG5Njx9iuu3+USbXECExFK9ki40BhmzeFpYTAaU\n2ptnc+Rc+Pn4wZcYX9DcN6I2fjTWOLXrMrh3aRV6SvOuNR1g+oxNSqdzjDltN5knqKrdjQowmRID\n0Yk8rPB3jdMMA1YcyWyOiBlAbzyHPyRmeDgeQ/HzXW210OJUfxyMcTEjJqAwEorbNh2/OMDBFjG3\n8cUAIzZBHV/Moc3yS9bunV102OSz0Nalr0pjUfC4lBSu+FTnGZKE07gixtZVSnWs9bfQZTYiGZ/D\nZzHIg+cvHfu53u0hmNF4b7SBQUJzUr78HPYSeYSNww14VRo3n0FXBbzSQlapRkMKuWWQJAkKQ/PI\n2BJ168qyVvZJgV6X3qfAb0PryidIIORn2GpFOD0htmJ97Sr6XWIjs8zAGLr3cezj8iYx2UHUwv4x\nja/TvgRfVsaVLXDtN2apxYMHxKAe7h3AKFrv4s4lBOHyzFSReUjTqmWOQhBw77wAMG26T92gBxkR\n0yGiCJrX3MtX1vFf/Fffo9/LAqMBsTmzyQhrbFD5zr27eOcelRKsra4i4fnYikP85V/8AADwL/+H\n/wm/+ILG8pvtD6FYvZqVNZMZhDGYrIM2FngNOcjeQc2un5/sITDcJ66lcMbXOZ1OnClsnmd49oyy\nDffvP8IGq+Qub11GzN5uIghcEbc1tcfZdDqHZXb36GQfG6s0N3rtFjqqcustMOB+i9PDx/jOhyzi\nyBMMzoixKgIPF8xOL4M4UCjZkDjJc1for00BwQIDW2pINl3WpnCmshaL3oZmoatO/e4KYR0bqHXp\nGGMh1cKeiprdMwbDE1pfzy8PkHAWqNNuY4cFUFHUws7lK0uP8WsNpgKlEHO/OR8ZSh5lCh9Pj+jB\nH5/MAVstdGPc3qRF4cp772LrUtXw0uL5C5pMXzx4gZeHlBYaX5xhPOG8dRgg5vyrpwI8fsIO0ofH\n+L3fJsPPKxshLKdz/FAh4w3LKA+ikqcGAtJbvs/S9vYavvMd2vS//2d/iyKr1YjBKv28c/kaLu3Q\ngiVDD6MR0Y3HR8/g8XcZYxYMKNWCIVn9XRbgegtwPy76t5e3NpFzsHltJ8Q37u0CAHIV4N13qXbs\npz87xPyAFv+VfoE4fGup8S324wNeDaCq39Pk58kshHNAH44Osb3K4xY+LAcpEgE8pncnkxk2Wdoc\nBhs4PKLgOynnGDKdvdrfhOWXbn1lE71V7on28hkueOOVnoJxDYfhFpBlkKUzRxNbU0Dxwmishuag\nTFvhrDSsMDjmhXoDyqUF03SK7hYZOZazIWZzVnMqoMVGrXF7BWAVTXr+GGZGhwptSkw5tZcbiSFT\n85PBsVtYFp2QjQwRt1pLj/FGeoSMDS2TaYYJp4XOxhkmrEoK2210OHBY7fUwY5XXfDJFn4OCSCmc\n8z3PtcGMA+THJ+c4S+j3G+0YmlVDo4vUpflKbVydm1EScw5gz0czzLiv5u2d6zj8218AAE73ziBe\nQyXV6feR8f15enCIkoNW6fmuCW+gBDmJA0jmE6RsQLq2to2Qn8tMWGTcRFhkAyRj2lCu9hRu7O4C\nAHbW+hhr2gQ319roKxpvnkxwOKFnNw9XUYrKdVxCVA26rYVAFTBILJswmKUpEk47R3GAixF9z/b6\nmkvxRFEMxbWJeaZdEBlGket/OB5NkfDzv33rbYCD2iybQlWqZp1hf5822HZnFbxsYm9vgHNWRR0f\njTBlY9dZUWDO82V7/RLimNPIqgUhl19PNzbX3VikrB3Hrc0QcJ1iZlIgpnu5df0S5n9Of39pZxX/\n6T8nSXu3q1BWRsZl5lK7Sskq8wMhJmh5lal0it/+ParDEt6/wPf/zV/Q2HurUB1K429ubSPlQrUk\nLVG1ktNWwxbL19mOJnNsbNNnnp6e4ud/9X8BAL7z1mWcc7nG6GLk5P5xFLlAY7trEYIO6QePj+Bx\nkNJdW0OX6yCFlWhx4Hxw+MQpHD0hcPSU9tHW2hpePKX1Jivn2Ix+nz6/Dew/Isd3URp02Vl/WFoE\n0fLGpFev7MJO6Donc4vVncrYGq7JupEaVS7eh3a1ctLC7cfAgvJVGki21BGlrtN52taNoD0Fy3WK\nRvrQOVsS5dO6FAXShb4q9LF7h/dCq3Ex3l96jE2ar0GDBg0aNGjQ4A3wtTJT7VaEWzeINhuOc3zJ\nbJGARmApAt9qreLGZYp+b13v4vo20ZBR5ENwWkUIgzs79Dfv7O7g4JSYqc8evcDDF8TInAyGaHXZ\nJEyX+PRz6r2zvrKOjU06/fe6XYSyLlRTHNVbFaLls0+L1gudqb8aWT7Dt791mz+zxN/8NSkWT4/P\nkOd0EpRKwWc/kNOTQ2TcAseTITzBaQlTusi5LAXEQty72L+r8l2BFZhzeub5ozPMSxpjXAQIPqBI\n+9ruTTx5RiesyE+RMENw9dI2uvGyU0HA0WMWv5aZIjbKuT+55zaenGI6I2q4E11CFctLCcQ+nZba\nrQgJn27X11fRXqNr/PKTv8KjQ6La19euQDKr9fLlM2xt0HN++/pdHLKJ6dHgGJZPLfY1TvsAFZe7\nJ65LGFEJE4TzSCqL0hl1Ghg8OqE5uH5niO1VKpoXRYpnz58BAKZzAcOiiWKWYk3R3yuhXM+qdDpC\nMecCYaMhqrYJsLDsf1QUGfKqgzoMysrEUliIfHmhRCyn8GI++YU+gjViDnQ7R4v9nlQQQJ/R9UAB\nJbONYRjg2mU6SfuexKMXlPLZG0yQuV46ElNOs3daHgaz6saVGF0QizfNtWtVYYVxPjrTmcaDB5Te\nPx6PHKN39foNBNzZfhnk8PBsSNc/PD9DyT51YeAhZsUirHan1XkyRcC+drEKXGuR6dk+wH0yA1tg\nJaRrvntlHWst9kfzcuTcPmXn2mXn86ZlBMGtV9J8hEmL/aekcuk8akHEtIZF3S7qK1AUJQSzDP21\nNWTn9M4Ph0OssXIKVi7MWVJzAYAfAgWzKpPJDOvrxBivru4g5XZFQRRDg9OwxRhJSs/tfG+I1Q2a\n42HcwUef/AgA8Pnnz9Hmli1vf/AueqvEPqy0L0GxssYKieI1egIl2dCpqaU07p3TxRxFRvNd2wSS\nsxD91cC96l4gUWhiQ7IcyLn8wpNwJplaw7XYIdVYJUwwsKwU+/Z3v4lr13cBAP/m//g+PvqU1vRb\nN3dRWdCdnw/QinnPgHEKu2UQRW3Ebbpvd+/ewZO/o73w9rV19BSNfcUP3FobBML5hfnRJYx4/yvb\nIabc3mg2eopZXglb5tjaoiLvthwhYxYyjGN0uGXL+dljgL3miukAkwNSHv/eH34HU+4LenZoccIZ\npP1pgdwuv97s7x/jN97fBQDIyEfJrGxRFE5nLQLp2p15UkBw2i7wPZemtAaOodNQ8KvC/QXvuKwo\nOdUKxIHvTHO1LTGZ089TIzHmIv7B6T76XA5wXkzhBxRbeMri4OB86TF+vWo+oxGzkvFbH76FNm/g\nnsmxzcqynY1L2OjSjdjsWRjO/RshFvq6GVdjsLPex842babv3buJvSNKXX306WN88ZgozOHwCFFE\nlKfX6uHwgibo58+H+PAuBXeR0gBLsDOjEHGMossSebG8aiGZT5HO6QXeWI3xzlt0bYE/QJrS7+fp\nBM+e06S3tkQ6Zwm2iAGwpcFCrzwhBDRTlaXWrk6KUKuSwE068zlwkVI91NXNXYx5ERk9eI48o2sY\nXTzC+grVL929tYYyX94McRHOuG0hmAKso9HJSoFWnLyY4eyc1G1rt66gyOlv2u0YIX9Ou9VGxHL8\nje1NsDgIZ9MzTFJ6tnvDA0QR2wlkGeaPqQ5kvbeOLaaPQ9/HftVTTxfAkgopACiLog6mjHami8YC\nKW+wVmtXM6WNxpSbHn/y2QPcvU4bTSQFJgOiif+fv/wRbt4iRdjv/PbvIOdAI81S+NwAudXfwLxy\nvZ+cw5OVpFchbNHCaJSPWcaFKQKoCgL6rQCt9vJpvoOzGUremLxWG+0eu0lPhjjlugh4PjSnqxB6\nmJQc0EmFlE1elfKwysq7USEQl5V6CtB8/aPZDBnf/6TwkfPCWHo+Ag4YlYTTeHWUjw4HOyurXdz+\nxj2+D56zXlgGqRbITCUbzzBjt+SLiwE03ythNRJWcGmdIeV+mPpkH4qd2kU5h3ClBxI3rtGa0fIM\nUlZcmnSMHjeH9dFyhrFxpHBtSnPmIh0jC2nRzqDgoarvMzDOOFQ5p/avwnSWYMqNqNvdPrY2ad4d\n7e8h5ybT/V7sJPLCFphOKbjsezEyDkZGoymuXqEDoB9EyNh2pt9bxcWY7tloPIbh1JUfKUymdCjK\nS4W33qXns7VzGxP+tyubq/A9mlOBjF7Z6OxrdCOYTC6Q89okPQtZvRNljjKvas4MBK/R/XYLgvN2\nQRTAmLpuqHLOthquLylgIWzVV9ODx2nkeToDJNfY2RTb1+i5/f4//TZeHNAalqce5my4PB5doBVT\noKyU/1qmnUmW4cUerRMnzx6hw+tfNwIGz8mF/XZlPguyx5GcpvYihd1q79zcQcBrSW4sCn4X87TE\niBs+K+VDiqoTQOEack/SAv1V2qs8abGzSeONVY5zXg9ODwvAVmrwDaSv4YA+GU3wP/+v/wtdg4Qr\nWxFywa1fCSi+NuV5rruGgHUm0L4KXD2gFgqerNK1NclgjXC2DYFS8NzaXyC1XFIxPEbK8//vfvID\nlPyOXL91G3/4x/85/XU5x9nJ8vtik+Zr0KBBgwYNGjR4A3y9zJTVEMxStMMAv/E+FUPHVjs/ED9M\n3YkAtg3BPieQ0pnAWVhIPll4SiEvKHqMlMGNdTrBb/zWN3DpMp0U/uKHP8WQKT05m6Izp9PKg8ME\nOxv089uXOvCY5RFGOiJDKgNtl4/AAYOQU3iXttawzqadH3y462jF6TSFxyrC1ZUe/uiPqEgy8COU\npvJU0c6YDRbuFDkejV2nbK2NS/9ZC2jDdv0lMOGUYhgLDCfEUrU7Gwj4JPLee9dx7QqddnZvbDql\n5OuAWCfmcIRd6ExepwK11nUXdGEwHNKpbnxygMmU7n3Pu4HeBvdx63jwV/j04M3x8/s/AQAkZeoU\nc1qnSKd0PzwjSdoD4HQ6wMV9usdXb9zAzZ1dAMDzo5dIqorZJSCUgmD2LzPaFTNao6G5iFkbg7RS\nUhoNnyfMo4cP8eQDOql/cOca1vo0NzcigZMDak+SF7+J9R4Vbxrho+Rra8UhFLjNxXzkVH4aAiGn\nMbrdDoYjZkOMRczeQnHoIXyN5nwnBxfwuLhcJXNMpsTilUZglYtM22tddNfpJDoczXF6TmyEF4Y4\nn/CJfAqkzDR5gUAlKcuL0nnnTJIU40rtGMTQRVXQb6FqggDcXQq+Ai57dDq/du06Jqs0ly8OLyDL\n5ccohIStelEKgQkr9WbTKTSzuN1u281hW2Roc/o4O36G6cEz/vsJBBdrq3YHvZjuW6fXq9lDYzDi\nFPPe/r4zPl3tdxFZ+rcbKsKZoGdXtvq13x0EKi9Pu9Aq5KuQZDmO2DQybkXY2aZ3CNsCkwmxuFpb\n578zmAxwxixDt3etZihKiykrNS9GQ8eazuYlSlMx6BKCRQee52HOf6+8DtbWicWYzM7R5aLkrChh\nuK9q2KrNFQPlv9Iq5KsQ+DGqtcSiBGyVJQjQiquyCQPFf7O1so2Y/dziOEYcVZ5QM4CVoMLKynsS\nZZnDsJil1AYFp668IHDsRlEW0JwxePveZfy3/82fAgD+x3/5f+KUFdH7L/exsclKOguXfl0Gn748\nqIvLrcLmNTI47a0G2AiJbdza7KPg96bd7iLjvUH7Cl1W8LU3u64VlB+GSNgYM8tLBDw3PS9AwCUV\nk8nYpc9iqbDOa7DnKYC9FU+TEn/3jNKIP/ryDGPem+cYIluynysA5OUc55wunIxnzjzTWk2KHAAC\nHuqmWCWqYnQpvNqTTXpOmW2lcGyUsSUMq9+VNDCc29ZVHhbEglXmtEr66LFHW+CFKNnk9uDlHkZD\neqZ+oLC1XTOCX4WvOZiSkFUFvTFOwQIAghclgwKaA6jE+nBdYIWGcBYFFhWpZmFhdOVq6rlapMj3\n8c236Uak6Qz/7m8+AQAMx8co9+glLHWEB89oMbq+vYNOzHQjNDJ+4YUQUGV9nV+FMAxqY+OWgdG8\nqIpVXNqu0kUWGTdIjeN7dXNeuJQxObfyvfKV5/pglkVRqxCEhOdXC6J2E0Vr7VKTuixQVppd6eHt\ne2RqGkXxghGafI0uSzVekawuOqMLuSDv1m5MkAYGNO7DZ4+hJTeuljmurtAz2d87xhc/pdqoiZ7h\n8IB+lhAQrDKiucOGbhKYCa6xWutDn9ML/tmn93HvHtWK3bx8HY8Ony89rk47RLWAFxmcgafWqFto\nmQLGVtYLEkrRAj55forPuD7vypWbmE9oQ+lFwOiCNqAnTx9hkx2yPS+q7S1UjKIc83dp2MpF3tZm\nhSv9Pl7sk6RXegZ9NrWNoxgBbyLLoCyAkgOHVhBhxvV2NozR36b6mbDt42JAi3Axn8OrGozmOSKf\ne60JBcENfJPkHPO86hkmoKsmzEohq+qP5rmj8oXVbhNUUiLgA0ZUGhzt0RjVs+eApXqPbDZHt738\nGH0pUPCzm+kcZxNKcV3tRu5QMRvPAG5kveIDXd4gfJNjMKFNJE0K9Fh+3l9fRVhJrVMBsFP+6eER\nTg/ZLDJLEXPfu1hamJQ+ZxMKR2wHMy0DzANOLy0eRAwwHy9nFNhqd/D4CQXorbCFfpuCmvW1bRdA\nTcZTZAmlkD777D7avMbl2TYEO7YLeDg7Y5m7F6AqHaB5x8/Hi2G4ti8vC2QccMXK1s0FhEDJNvzd\ntXVnI5IXM3R7bEdjALl8LMVdFap7IyAF17VKDcG1MFJ4TgXtRzE6bBC7vbEGn9PIaQkno1dGoOTn\nrHXp1GFCSpf6lsKv33VogI1Ds/EUa+wIvtrV+NmPvwAA/OSHm3j7HgVBnq8Bu7xNyfOzoUtNRnqC\nbW4ifZxE2Ih26eeihYxr2ebnOQyXpASdFvwJjfHx+QRxXDW1VhhzbRSkckFTXozrBvCwdfmrLWCf\n01zKssyteUVu8fyU1q1n0whTQSnxEhmMXX5fLEWKjUt0TwqdIk3pGi5f7+DuO/RuCZS4GPL+sJ8h\nmdM9aYVdZFzXOB4PXQ3uYg/LUCpoNv21KgA/dvhKuZpn5Xnu4IpSI01pzTNQeOsbpPB/+fIZHnz+\nEQBgZXUF29vbS4+xSfM1aNCgQYMGDRq8Ab5WZqos4NgFaINSVyoNCR9VIap0Hj9zmSFitZrnKRdR\nSyGc+Z0ScCeUOGpBsTeMMRYB99n67of34HEK5C/+5nMMBkQ3BtbHY2Z2ru/ewYdvEVsQeSn0nJkP\noyG95W9TkmTulCmFgOQTx2JPPc9T0OwJ5PuBu376Z2x66Pl1azljXbuHoqg7z0upnFKBxlxH7IsK\nRMd8KeXUX4t+UaY+oHwlFlk0GDj2ofre6r8LgsP698a6ruZZUoIPujg7P8PxIZ2evX6M6RnRrKfj\nM/h+9Rxqpg5SuNYgi70CJ8kMmxv0DLWY4VNW3bz7zbfQ7S/viZIWGgHPCz8M4VeMmDAw3L6gLD1H\nJVtdosuqrtVeG1/e/xIAkE/GWA2YjdSF6w338OEj3LxOrOn2lu9ShKWyMKwai1od5HxyslKjtHSz\n+qvrWF9lejpUWF8ntSuEgP8azJSGB8mpiPG8cCZ3NpAYcTuf6aMzlHwi16V1qahplgOWrjP0fCR8\nakyL0s0vbaxjhZTvu/lQliWZqwKQsPBkla6XMMxmDi0A9pkK9o/w3k3y6mrd3XZFqUuNsSyR8zNS\nfoScvWpya10ro/TiAmvMvlzud5AO2RPKAmGfGLqtu1cRckogihXAJrQlJDT3Coylwq1rJOgIFnrd\nlbpEwY0vNzsd7LKxrochMlaRFUKh5FRHYSWOOc3wVdjY3IKnKk8og6NDunZfbaHPTJqxhfPOms+n\nmIxozb11+wZKno9BEGLCrJ2MTuGzqtn3IvjMIkqpkOlKlSNg2ZNvNkmcn1u71YVv2Ii38N099gML\nzem50tQ9PJdBXqQo2G9NQsBnBkoI6dohGQlXlGyFRofFBZvrqzBVe6Ayd2lwaQHDc1NI7fzilOc5\nNkpbU/+sNYq8Xrs97nu6tdPH7i0q/j45GeDRA9pXdm9dQc5tkpbBSqfr0oKq8KBZgfiTxxoB9+YL\nw8hlYOaz1BVwR77vSjSMMZCC288ohZQVhQbGrdtaS3jVfmat63VotKaetACkbC20tRrB+rTGtDot\noCrgLrTbv5fBRr+Pb9wlkcPRwTkEr+tv3duG51XrgUCVf907GOLnn5AwazaT8IPK7y7COmcxPKEQ\ns4Ly0k4PZ6f0Ln52/wBjNgidzqeuzVmZwcUQEMLFGd2VdXTYaHljYx0PPidfu83NNbxz8x8uPcav\nNZiapXOAe955UiJkWt8XHjx+aT3lvWJWqVxzx7oZpFIKUlRBh3QBi5DKpUN8oVxuWPghvvstullG\nB/jzH/wMADAYPHOpw5XPurh78x8DAPrdPgrND2M6XrqGAQD6/VXX00tK6RzWF+uIhJAoWMaeF4WT\nQgsh3aT3/bq2oMwLmKIyq7PubyolRvVdTs2wEGAs/qxt3TPKU8p9joVwL9VrQbj/eQX0fZWNBRZy\nY+REDQCeNtCcp261PGch4HUC3LrMm5IvnSJPCOtqpiBeHWuFEhYXBT23lc0V+Jx+miVzlN7yz3Bz\npeXSlNYamColUOTO5bjMDZKsbpJdsNFlRwnMucxvPDiEDqpATCDge39yfIoHj0h63I7a6PZY4ZWU\nLhWxEFdDQgHco8v3BXZ3aQHP8hwe1xaFrRiTfPlI4zwvkLACMWy3nKQ9DGKEVV2BClDyezYPSlTi\nKSsVwKl4baVr8Gog6z53Ol8I1k29aAOulkpbDVE1x9YGZWX5IISbM53DI5Qs/fb6PRR6+edYlAWc\nQl36uJgxrW/qd2c4m+DmNQrAIQoIVuSplXVsrxLFH7Q6kNVaAu0Octls7FId127ecPL2XGucj1mS\nP08RtSnF1Wn3cDdhc9fJAKMLOkCMMoXEo0B4JAOY4dFS41tb28TWJqVAk8kEh3yfgsjDNiuchZUu\nuPj93/0dPH78DADw8OFT+JUdSaeHw0cnPxyFAAAP10lEQVT0+/76CiRPvCzNUHB6VimJhFN4uiww\n4XSMLg0sO2e3u9vo8D0w8FzphtZzF7gp6b9GKAXM5zNoVo5KK1DIqhtB4axPjC3geTT22WzkAnQB\niyytavsGiCLu82pCiMq1X9V1NcIolLpat4RbH42xrnZXKM8FAjff3oXgRuanxwmeP6XN/9q165DR\n8uvpSqsLsBVPIHuIuG7SCB8pKIgoPc/1b1QbnmvAa4sci1rzShWqyQ2T/sbCqX6FMTALJ11TVubK\ncIG5tYY6cACAXnX3IRahqyuOSg1TLt/P9Xv/5I9RsMv4nSuJIyh8v+XWACmsU0jb+SEesau9lALp\njIL9P/6Df4T33tkFAHhCQsiql18BwXV53z06wxnXd15MR65LgYJw9itRq4Xv/8X/BwA4H06dyWqo\ngH/xp/8lAGB9pY/19vLp2ibN16BBgwYNGjRo8Ab4WpkpLS26XBzo+wKGjQilkVAunec5RkZrvcAQ\n1CkmKWUdOQvpfFk8z19ghXzXYb7jR/D5BPEH3/5NhMym/NnPPsbZ4BkA4OPPLH7rQypYvrzxARSb\npc3nc0xny1O2UdR2J3LfU65NCqX4KrYIrht74NcMkRBwZnLGGiiO3j2pYFRdEF9hkXUSQrzC0lSM\nnqeUoznzslamLcLCvvK5X4VXunCL+neLn+GKOoWphQPWomCGouMHUD7NhbRIMak8m9K5675+99Yt\nnI8o5TFNE6Ay/lu4lleuWwAlixpGyQQbfDqXymJysbz5WjEfOD8ja2rWLs1S59mT5dr5FuWFcUaE\nQgist/g0aYDcpV6NOzGbosCjp495jDcQxlQIrFQEKSsmS0Jyynp6MUDBjAZsiXblJ6U1NPtSTXOF\nw0G29BilNFhntevW1StOIZOOZ5hdELs3Op9Bc+uPo9MLzBJidoSnUDIr0Gl3EWk+tRuNEZtCSlWL\nGqyFm3cSGpqfke95cO1wTD2XElPC5/Xg+GKGp3vETt757rcQdleWHmNhgKIaV5pjnVnrDz/8FkI2\nYp2MzrFzexcAUI5PccHjmubGnXq1MrDMjuRZiXdvUdoxCAROT+ja2nEHSUSsTAELxT+v+4FrG5Ml\nE3isys2yOY4viL0KOl2IglR2cenjnr/cu9jrrqFk1ZVpG0y4fc+L4zO0WC260olRsE9Tr93CBx/8\nBgDgywdf4P5DFncIHxdspNpfW8eNXWLSYAtXmlBog/09KrAfz+bO96e3soYW96rTQkAzT6Kkcekn\n67cgOE1tNDDLl19PjamFNTCAYWZ7OLyAMZXyTqBV9U5M5m68vmdRsO9VnqbwmcWVQkGhKsgunEpO\nW+lYU9/zIXkOSglXIC6Uh7LqI3p1G11O/46GwI//mkROL18e4fbbW0uP0RPStYcBAMPfpUUBK2ne\nZcY4JrGwxuUDlCxd2xWxwNhTpoLTfwvrLkS9ZxhjXIoWQN0fErV/YGI8YsYBlDpxqUBjLcRrZGzy\nQuOnn1Cx/pcPHkIvpJirEhxr6tRqkiQ4G1LazgtbmHD/2v/3L3+IX3zyOf3e82qfw4U9zFM+9ELJ\ny+I9qfbaTjRF1RNpMp1hjzMjeZ7hwQP6eefSNqJbt5Ye49caTM1zA99nelVKZ5MAq9xCqrV2ygNj\nzEIKDK9MgnqhrhsfKqVeCaaqprfGWPg+vcyh5+N3vvUBACBa6+EHP6G6mr2XL/DsCW1w33zrLXcN\nURRhzovUMnj65JmrVxKibqRsTJ2eM1qj3aEFaH193aUciiJ3faI8z3NRgxACPkvjpZSOMi/KAvaV\n9Jx45T8ApUwq0zLh+cg4UKHef3xvrf01ybrXh30lUKs2SbOQurIo+AUMohYU1/hkkxwZ14x0Ol3X\nbPb06BxXtkka/PD5E5fjhsSvLfKqlwKgFAZH3KtMmwxWqF/9B38Pktl0waLCOno9KzRSVjGV2kDy\nWHxhYTmoKaWF5wJigYJl16kWSDhPFrU38M33yT5htddFzjU4YdSC5AVfBQYp97wrkhHylFU0QrpA\nz0BhOKXg5eXZAebp8oubJ6UzPs3nGcYssZ9P5hhe0MKVZAYiou86Pb9w6ZCyKFyPtCgMELNBYeAp\npwLyg8BZexitXc2fJ+BsHpTykGWVzYetRGSA0UirpQECBxx0dHp9bHBd0jLITAHL7/G12MP3vvUe\nAOD9u3ecncrlyxtYY+Xd48EY51M2u1zZRsbvfVkWsFXD5yTH6SnZDgThCtbvfpPuQ9RBWaVVhEbI\nwZfOUpRsCmpKg2TKDWfHOY6PKYC6uXkDEd+Tl08e4NKSLu/tdgszdpYPwhArK3R4mIzHePyYLEg2\nVrsQnKa25Qlu3iU7mvc//A1nn/DlFw9czc7DJy8w5Rqrra0tt54OB0Ocn9PfKz/ExhalRsO4Xdcr\nWUBX90lot6YEQVAbLkuaM8vCWuNSdUpIZ+fQbrdR6qpeLMXZKQVoDx88RdXvW8kSw6oGLkkxGVeN\n3T0YtjrI8wRJwpYyWrpGyq1WvFDj6iHgD43ieKGvn0CvS2MpixInJ5TmG41GKMr20mP05EJ/C6VQ\nJYwCIRChCvQKoDJfFahrByWg3d5ZuvpbAQVbGc0KhcqqnRTY1f2pD90AFuqqNL2PAHIh3LvuKQNd\n1akZA/MaBYx/9bMv8dOf0V6bZanbw2i9/tXyFLNgaVBOz511xMuTCxxdLOzH1TkdtURUCM/NvepQ\n/8v/wJMeCr5x3dU+5lzy4PkhPv6ClN8Pnh7jeEBz779bYoxNmq9BgwYNGjRo0OAN8LUyU18+fIkN\n7mR9d3cHrapni/1lVqP++dcVUgMWihUy2iymlIRTKlgLQFT0Z10wa1SOyinw9s4O2r9Hp8Anz09w\ndYtOdgoGik+KnU7ntdJ8p2cnLqouigITNliUUjkGajqdoMPM1O3bt3H3LUovSiHgV+1hCus8MYQQ\n8Ph6rFLu9gRKuZM3MU1VAW99B40QmHG/piRJHesjhHAMmrFAECxXaPfqYxIuLWl/JX1Yp7fsAuWq\n2fSt1W0jblHKZn90jpIZnyAW0Loq1B7i1jvUuuOoc4pBleqC+JV0p7u4yoBRGJfy09bCe41jQ5LX\npyJtTF10boU7/5TCQvMpMCvhmLWitChKTs8ZzxXt5mXp0q3bK13sXqVxtQMPWZVmyFNITo3kWkOz\nh1Gn23IqyOF0joLT4Flp8IzbJ61ffwurxfKvs4i6ACuvzo/HmLPK6+x8hPGMTn5eEGM0IcZqkqVu\n7HlpkLOC71xNEXH6YT6b1+xhWbr55fs+yqoIVArnjZYkGXRdIe4Kn2ENbGWg63s4OSEGZ3A6wJVb\nu8uPMZujw/PwG9ev4laHrvP42cdQbHa6sraB54fE4vz48wd4tk+prHt330LGJruq1OB/iiI9R8qp\ntfbKKjqb1CKo0AZ5wu9ZnqLglGgxGUHz80qSHMMx3dssy2H4c44P9nH9Gvm/WV26/nlfBddTDlQg\nHrGJpdYKCbeNOT6ZOOGDkhYP2JdqZbWDzU1a7zor64CiNFleFjhh5m0yzdBi1bFSCptc7O4FEWJu\nbySV7+a1VHWaSevadFhrjYjNufwgQKdTNQ76aiRJgpwNVn2pnD+YELZWMlsPRwc0R3yvhd/5LqUy\n2x0fZ6fERmljMWYWN4pamM6G/A0LDL3wnHlpkrRc6504jp2Jc7fddmxOXuTY2KCUaJ6XAKeO0zRB\nlk+XHuN4OoHn0bUJKSE5GxOYAD6rJkl0w2utWPAUVBJaV9kP8wrT5GDhergaW/fApNRYXTpTsVrU\nr5C94+QckscVKuuYqUwD1izP9ks/wlvvcFuoBbNnaxdLNX513wfonXDPSHkLf1eLuuyC55UlV8Jf\nuQYh6n1DycCJR6TIscrpWuX50FV7IaUQxcsXoH+twdTB0RDZjB7MpY1VtDnNo4126QGqU1jcKOm/\ni1J/Cp6qxj31A9VaO/owL3KUvNjMM4uCJ2UpCrc5drwI794iuvrO7h30I/rZlrmjNrXRtZR0Cbz/\n/nuOGldKYcoLcpqmr6j8quadpEzknK7nO9dOa2u/AoNawup5Hjxucmm1gfB+1TJBSIGy+hlksghQ\n/VeVVhECbrGbJxk6neVSCxSv1KZvLo6BwSLR6VJ71tQTXVpornMQvkSoKoWM72wGikI7ej3yQhw+\nP+XfG1hHYb8aTFUw1rpcOZDXtQQQ+HVpwb8Pn53kkFxL4Hk+DL8mhRUo2BgOsoWU+5/N5oVzjdbG\nuIBCmxSdDlsarLTI0BVAu+0DLPGOe+sIWamXFcall4vSQvVIkhzLVdgRLf6R9VGMabMbT6Zo8d90\n4j6OBy+XHmM+t8i4dmXv6AhD7t2VZDkEBzK9FQUWKUJLhaSoqfSqXGKSpM5Z3MLC41RBkefuORpj\n3DsklXI1KmWhXRNeKZWrzbBGO1VSaSWmM/r8j3/+Ma6//dbSY5xkwIRTbH/9+VOUvEH04wzzKY23\n21/HhN+Jw+EcZ9wh8NEXzzA9pxRRNh7jUo+CijYSeJweba2M0e5TSnQ+myPlQ5fVCba6tAivhx58\nWQXUQMLpE6MUWmyaebz3Al7V4BXCpZ2+CqYsUeZVk17r1o5Wu4OInb+l1c5pXUkBw8FlkhfYO6R3\nSwgfqxt0sDFWO8sBIQRCdsm3xkLxe+B7oUu3KRW4Zw6YhZrPWimmoWENBRdaB66Z9DLYOzhGzoFp\npxXBq9wYhXI1bUoGCHi8N3avI4jZNqAbIOVroL57cP92Y4MOM52uv/CuB1UmDUCIomqMHfpuI7K2\ndOv1aDjEnA+qpRG4eYeCTRkaZNnyKfckSxHyfqOUhGIrlhzSrT3Wakju16oEULrrEc7iREjj1n0l\n633FWOPWRSm9Vw/A1d+gfqetlahcL5WwkKKamwYuF28F9GsUh/zpf/2fLcxr64Ids2BBsSgOFxCo\ni0/qoAlY+PtXvn5xgZfueVGAWO0bi39VB1xSCETc1WBzfc3NE4Ha2mgZNGm+Bg0aNGjQoEGDN4D4\n1fRMgwYNGjRo0KBBg2XRMFMNGjRo0KBBgwZvgCaYatCgQYMGDRo0eAM0wVSDBg0aNGjQoMEboAmm\nGjRo0KBBgwYN3gBNMNWgQYMGDRo0aPAGaIKpBg0aNGjQoEGDN0ATTDVo0KBBgwYNGrwBmmCqQYMG\nDRo0aNDgDdAEUw0aNGjQoEGDBm+AJphq0KBBgwYNGjR4AzTBVIMGDRo0aNCgwRugCaYaNGjQoEGD\nBg3eAE0w1aBBgwYNGjRo8AZogqkGDRo0aNCgQYM3QBNMNWjQoEGDBg0avAGaYKpBgwYNGjRo0OAN\n0ARTDRo0aNCgQYMGb4AmmGrQoEGDBg0aNHgDNMFUgwYNGjRo0KDBG6AJpho0aNCgQYMGDd4ATTDV\noEGDBg0aNGjwBmiCqQYNGjRo0KBBgzdAE0w1aNCgQYMGDRq8Af5/18qbsYPIRp8AAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e0f61cb748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 130.64189796  135.98173469  132.47391837 ...,  126.64218367  125.86195918\n",
      "  114.39957143]\n"
     ]
    },
    {
     "data": {
      "image/png": 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/wB9ExOcZLce9XdLVwO3Avoi4HNjXfG9mp4mxyR8j/918e2bzL4AbgL3N9r3Ajb1EaGa9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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e0f3d24198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.209689\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -38.022230 analytic: -38.022230, relative error: 6.981206e-12\n",
      "numerical: -5.893032 analytic: -5.893032, relative error: 7.226776e-11\n",
      "numerical: 5.098977 analytic: 5.098977, relative error: 5.410257e-11\n",
      "numerical: 5.563215 analytic: 5.563215, relative error: 4.931172e-11\n",
      "numerical: -56.345541 analytic: -56.345541, relative error: 5.522180e-12\n",
      "numerical: 0.247767 analytic: 0.247767, relative error: 9.471883e-11\n",
      "numerical: 4.913731 analytic: 4.913731, relative error: 5.613825e-11\n",
      "numerical: 20.310017 analytic: 20.310017, relative error: 3.684332e-11\n",
      "numerical: -2.436214 analytic: -2.436214, relative error: 6.264005e-12\n",
      "numerical: -42.927566 analytic: -42.927566, relative error: 4.946853e-12\n",
      "numerical: 10.701603 analytic: 10.701603, relative error: 1.592050e-11\n",
      "numerical: -8.439286 analytic: -8.439286, relative error: 2.539742e-11\n",
      "numerical: 5.242355 analytic: 5.186827, relative error: 5.324246e-03\n",
      "numerical: 9.253970 analytic: 9.253970, relative error: 8.011196e-12\n",
      "numerical: 30.223619 analytic: 30.223619, relative error: 1.543282e-12\n",
      "numerical: -26.673312 analytic: -26.673312, relative error: 1.950953e-12\n",
      "numerical: 2.178950 analytic: 2.178950, relative error: 7.490076e-12\n",
      "numerical: -10.355818 analytic: -10.355818, relative error: 1.004596e-11\n",
      "numerical: 17.556103 analytic: 17.556103, relative error: 4.188620e-12\n",
      "numerical: -22.176325 analytic: -22.176325, relative error: 3.715027e-12\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "as hinge loss is not differentiable when you get close to the point where this non differentiability occurs your estimation of the gradient will be different depending on what direction you come from"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.209689e+00 computed in 0.126622s\n",
      "Vectorized loss: 9.209689e+00 computed in 0.008008s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.144639s\n",
      "Vectorized loss and gradient: computed in 0.008008s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([3, 4])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1,2,3,4,5,7])\n",
    "y = np.arange(10)\n",
    "print(y)\n",
    "np.random.choice(x,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 780.807695\n",
      "iteration 100 / 1500: loss 285.041761\n",
      "iteration 200 / 1500: loss 107.094042\n",
      "iteration 300 / 1500: loss 42.187618\n",
      "iteration 400 / 1500: loss 18.822262\n",
      "iteration 500 / 1500: loss 10.912622\n",
      "iteration 600 / 1500: loss 7.224100\n",
      "iteration 700 / 1500: loss 5.772935\n",
      "iteration 800 / 1500: loss 5.843052\n",
      "iteration 900 / 1500: loss 5.443497\n",
      "iteration 1000 / 1500: loss 5.165529\n",
      "iteration 1100 / 1500: loss 5.452788\n",
      "iteration 1200 / 1500: loss 5.197308\n",
      "iteration 1300 / 1500: loss 5.307823\n",
      "iteration 1400 / 1500: loss 5.922122\n",
      "That took 7.863023s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4TkRcDtwCbAVuAj4WEdllqK8ktq5v4+DwhEttSJKkc1aOIc6bgbsLx3cD713Qfk9KaSql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\nZP4FfN6tRRqVn4AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e0833fd1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.374510\n",
      "validation accuracy: 0.387000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Development\\Work\\cs231n\\assignment1\\cs231n\\classifiers\\linear_svm.py:94: RuntimeWarning: overflow encountered in double_scalars\n",
      "  loss += reg * np.sum(W * W)\n",
      "D:\\Development\\Work\\cs231n\\assignment1\\cs231n\\classifiers\\linear_svm.py:94: RuntimeWarning: overflow encountered in multiply\n",
      "  loss += reg * np.sum(W * W)\n",
      "D:\\Development\\Work\\cs231n\\assignment1\\cs231n\\classifiers\\linear_svm.py:86: RuntimeWarning: overflow encountered in subtract\n",
      "  margin = scores_  - correct_class_scores[..., np.newaxis] + 1\n",
      "D:\\Development\\Work\\cs231n\\assignment1\\cs231n\\classifiers\\linear_svm.py:112: RuntimeWarning: overflow encountered in subtract\n",
      "  original_margin = scores - correct_class_scores[...,np.newaxis] + 1\n",
      "D:\\Development\\Work\\cs231n\\assignment1\\cs231n\\classifiers\\linear_svm.py:86: RuntimeWarning: invalid value encountered in subtract\n",
      "  margin = scores_  - correct_class_scores[..., np.newaxis] + 1\n",
      "D:\\Development\\Work\\cs231n\\assignment1\\cs231n\\classifiers\\linear_svm.py:90: RuntimeWarning: invalid value encountered in less\n",
      "  margin[margin < 0] = 0\n",
      "D:\\Development\\Work\\cs231n\\assignment1\\cs231n\\classifiers\\linear_svm.py:112: RuntimeWarning: invalid value encountered in subtract\n",
      "  original_margin = scores - correct_class_scores[...,np.newaxis] + 1\n",
      "D:\\Development\\Work\\cs231n\\assignment1\\cs231n\\classifiers\\linear_svm.py:115: RuntimeWarning: invalid value encountered in greater\n",
      "  pos_margin_mask = (original_margin > 0).astype(float)\n",
      "D:\\Development\\Work\\cs231n\\assignment1\\cs231n\\classifiers\\linear_svm.py:125: RuntimeWarning: overflow encountered in multiply\n",
      "  dW = dW / num_train + 2 * reg * W\n",
      "D:\\Development\\Work\\cs231n\\assignment1\\cs231n\\classifiers\\linear_classifier.py:83: RuntimeWarning: invalid value encountered in add\n",
      "  self.W += -(learning_rate * grad)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-07 reg 1.500000e+04 train accuracy: 0.376122 val accuracy: 0.385000\n",
      "lr 1.000000e-07 reg 3.250000e+04 train accuracy: 0.368980 val accuracy: 0.365000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.360265 val accuracy: 0.376000\n",
      "lr 2.505000e-05 reg 1.500000e+04 train accuracy: 0.164571 val accuracy: 0.145000\n",
      "lr 2.505000e-05 reg 3.250000e+04 train accuracy: 0.102959 val accuracy: 0.088000\n",
      "lr 2.505000e-05 reg 5.000000e+04 train accuracy: 0.059122 val accuracy: 0.059000\n",
      "lr 5.000000e-05 reg 1.500000e+04 train accuracy: 0.119122 val accuracy: 0.129000\n",
      "lr 5.000000e-05 reg 3.250000e+04 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "best validation accuracy achieved during cross-validation: 0.385000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [1.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "\n",
    "range_lr = np.linspace(learning_rates[0],learning_rates[1],3)\n",
    "range_reg = np.linspace(regularization_strengths[0],regularization_strengths[1],3)\n",
    "\n",
    "for cur_lr in range_lr: #go over the learning rates\n",
    "    for cur_reg in range_reg:#go over the regularization strength\n",
    "        \n",
    "        svm = LinearSVM()\n",
    "        svm.train(X_train, y_train, learning_rate=cur_lr, reg=cur_reg,\n",
    "                      num_iters=1500, verbose=False)\n",
    "        \n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        train_acc = np.mean(y_train == y_train_pred)\n",
    "        \n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        val_acc = np.mean(y_val == y_val_pred)\n",
    "        # FIX storing results\n",
    "        results[(cur_lr,cur_reg)] = (train_acc,val_acc)\n",
    "\n",
    "        if val_acc > best_val:\n",
    "            best_val = val_acc\n",
    "            best_svm = svm\n",
    "\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "print(type(results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kP7tNRsqRNBVYFBFzJe1dpNrZwNcioqtI0pTb1zRgGsDYsWPXJRwzMzPrgQwJ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\nzwmNmZmZ1b3/D+lNL0qtlzaDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e084b890b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.376000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mx5OFXhQzNR7ldmVUoN8lFWksPRUdnsJ/9SfIbUElVUZvqWfUY8bWjXPN4jE+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\nuQru0X4P92ZqhmSwk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e084dcda20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** too many different types within each class, the linear svm tries to find the pattern in the object but as theres so much variety it just becomes a blob/merge of all the differnt objects in each class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
